{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "1.4.0\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "import d2l as d2l\n",
    "print(torch.__version__)\n",
    "torch.set_default_tensor_type(torch.FloatTensor)\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import  utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "situ max value:138.0469971,seawifs max value:180.867205",
      "\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_data = pd.read_csv(r'E:\\02Projects\\papers\\chlmodel\\data\\seawifs\\03 augment\\train_dataset-aug-features.csv')\n",
    "all_data.head(10)\n",
    "\n",
    "plt.figure(1,figsize=(6,6))\n",
    "plt.xlabel('pre values')\n",
    "plt.ylabel('real values')\n",
    "plt.xlim([0,25])\n",
    "plt.ylim([0,25])\n",
    "plt.scatter(all_data.in_situ_chl,all_data.seawifs_chlor_a)\n",
    "print(\"situ max value:{},seawifs max value:{}\".format(all_data.in_situ_chl.max(),all_data.seawifs_chlor_a.max()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    " ###  按照比例分配训练和测试数据"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "percent = 0.8 # 80% 的数据将用于训练，20%的数据用于测试\n",
    "\n",
    "all_features =pd.DataFrame(data=None, columns=(all_data.iloc[:,1:-2]).columns)\n",
    "\n",
    "\n",
    "X_train_g = None;\n",
    "X_test_g = None;\n",
    "y_train_g = None;\n",
    "y_test_g = None;\n",
    "\n",
    "\n",
    "# 数据等值间隔\n",
    "gapValList = [0,0.04,0.08,0.16,0.32,0.64,1.28,2.56,5.12,10.24,20.48, 40.96,1000]\n",
    "for row in range(0,4):\n",
    "    for col in range(0,3):\n",
    "        idx = row*3 + col\n",
    "        curCHL = all_data[(all_data.in_situ_chl < gapValList[idx+1]) & (all_data.in_situ_chl > gapValList[idx])]\n",
    "        X,y = curCHL.iloc[:,1:-2].values,curCHL.iloc[:,-2:-1].values\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "        if(X_train_g is None):\n",
    "            X_train_g = np.array(X_train)\n",
    "            X_test_g = np.array(X_test)\n",
    "            y_train_g = np.array(y_train)\n",
    "            y_test_g = np.array(y_test)\n",
    "        else:\n",
    "            X_train_g = np.append(X_train_g,X_train,axis=0)\n",
    "            X_test_g = np.append(X_test_g,X_test,axis=0)\n",
    "            y_train_g = np.append(y_train_g,y_train,axis=0)\n",
    "            y_test_g = np.append(y_test_g,y_test,axis=0)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "===========================\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "# 构建标准化scaler\n",
    "X_All = np.append(X_train_g,X_test_g,axis=0)\n",
    "import sklearn.preprocessing as preprocessing\n",
    "scaler = preprocessing.StandardScaler().fit(X_All)\n",
    "\n",
    "print(\"===========================\")\n",
    "# 执行标准化\n",
    "X_train_g = scaler.transform(X_train_g)\n",
    "X_test_copied = X_test_g\n",
    "X_test_g = scaler.transform(X_test_g)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "                0            1            2            3            4   \\\ncount  1772.000000  1772.000000  1772.000000  1772.000000  1772.000000   \nmean      0.001593     0.003722     0.012973     0.019982     0.020061   \nstd       1.000042     0.999264     1.007492     1.021334     1.021805   \nmin      -1.235701    -1.568849    -2.272409    -1.964419    -0.968113   \n25%      -0.681441    -0.714926    -0.787086    -0.627151    -0.636465   \n50%      -0.307139    -0.245130    -0.152485    -0.210863    -0.402720   \n75%       0.427679     0.514241     0.741948     0.342925     0.257924   \nmax       4.525668     3.983032     3.885003     5.553707     5.108024   \n\n                5            6            7            8            9   ...  \\\ncount  1772.000000  1772.000000  1772.000000  1772.000000  1772.000000  ...   \nmean      0.015248    -0.001690    -0.003142    -0.003452    -0.002995  ...   \nstd       1.027891     1.000355     1.000450     1.001212     1.002674  ...   \nmin      -0.724359    -3.102175    -1.948096    -1.309075    -0.890647  ...   \n25%      -0.556287    -0.328467    -0.631306    -0.660712    -0.660977  ...   \n50%      -0.405765     0.188701    -0.097941    -0.297871    -0.396775  ...   \n75%       0.103852     0.654724     0.615842     0.364539     0.220431  ...   \nmax       8.140492     1.768746     3.211707     4.626660     5.485968  ...   \n\n                26           27           28           29           30  \\\ncount  1772.000000  1772.000000  1772.000000  1772.000000  1772.000000   \nmean     -0.000226     0.011879     0.009455     0.009702     0.008147   \nstd       1.073283     1.056038     1.023535     1.020490     1.116014   \nmin      -0.248536    -0.704596    -1.462872    -1.423469    -0.243139   \n25%      -0.207734    -0.491226    -0.753136    -0.784952    -0.141125   \n50%      -0.138224    -0.256418    -0.251756    -0.223437    -0.070376   \n75%      -0.026559     0.164448     0.602041     0.716180     0.006673   \nmax      26.153357    14.471823    12.666717    11.989204    40.991026   \n\n                31           32           33           34           35  \ncount  1772.000000  1772.000000  1772.000000  1772.000000  1772.000000  \nmean     -0.000628     0.013333     0.007529     0.007630     0.000755  \nstd       1.063879     1.057400     1.016192     1.048522     1.008857  \nmin      -0.227820    -0.511395    -0.838377    -0.846104    -1.610284  \n25%      -0.212540    -0.453787    -0.672961    -0.586937    -0.694127  \n50%      -0.177121    -0.346486    -0.436565    -0.355526    -0.266414  \n75%      -0.042893     0.096039     0.396876     0.394738     0.593009  \nmax      27.866206    13.296703    10.486907    27.683404     8.905791  \n\n[8 rows x 36 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>26</th>\n      <th>27</th>\n      <th>28</th>\n      <th>29</th>\n      <th>30</th>\n      <th>31</th>\n      <th>32</th>\n      <th>33</th>\n      <th>34</th>\n      <th>35</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n      <td>...</td>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n      <td>1772.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.001593</td>\n      <td>0.003722</td>\n      <td>0.012973</td>\n      <td>0.019982</td>\n      <td>0.020061</td>\n      <td>0.015248</td>\n      <td>-0.001690</td>\n      <td>-0.003142</td>\n      <td>-0.003452</td>\n      <td>-0.002995</td>\n      <td>...</td>\n      <td>-0.000226</td>\n      <td>0.011879</td>\n      <td>0.009455</td>\n      <td>0.009702</td>\n      <td>0.008147</td>\n      <td>-0.000628</td>\n      <td>0.013333</td>\n      <td>0.007529</td>\n      <td>0.007630</td>\n      <td>0.000755</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1.000042</td>\n      <td>0.999264</td>\n      <td>1.007492</td>\n      <td>1.021334</td>\n      <td>1.021805</td>\n      <td>1.027891</td>\n      <td>1.000355</td>\n      <td>1.000450</td>\n      <td>1.001212</td>\n      <td>1.002674</td>\n      <td>...</td>\n      <td>1.073283</td>\n      <td>1.056038</td>\n      <td>1.023535</td>\n      <td>1.020490</td>\n      <td>1.116014</td>\n      <td>1.063879</td>\n      <td>1.057400</td>\n      <td>1.016192</td>\n      <td>1.048522</td>\n      <td>1.008857</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-1.235701</td>\n      <td>-1.568849</td>\n      <td>-2.272409</td>\n      <td>-1.964419</td>\n      <td>-0.968113</td>\n      <td>-0.724359</td>\n      <td>-3.102175</td>\n      <td>-1.948096</td>\n      <td>-1.309075</td>\n      <td>-0.890647</td>\n      <td>...</td>\n      <td>-0.248536</td>\n      <td>-0.704596</td>\n      <td>-1.462872</td>\n      <td>-1.423469</td>\n      <td>-0.243139</td>\n      <td>-0.227820</td>\n      <td>-0.511395</td>\n      <td>-0.838377</td>\n      <td>-0.846104</td>\n      <td>-1.610284</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>-0.681441</td>\n      <td>-0.714926</td>\n      <td>-0.787086</td>\n      <td>-0.627151</td>\n      <td>-0.636465</td>\n      <td>-0.556287</td>\n      <td>-0.328467</td>\n      <td>-0.631306</td>\n      <td>-0.660712</td>\n      <td>-0.660977</td>\n      <td>...</td>\n      <td>-0.207734</td>\n      <td>-0.491226</td>\n      <td>-0.753136</td>\n      <td>-0.784952</td>\n      <td>-0.141125</td>\n      <td>-0.212540</td>\n      <td>-0.453787</td>\n      <td>-0.672961</td>\n      <td>-0.586937</td>\n      <td>-0.694127</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>-0.307139</td>\n      <td>-0.245130</td>\n      <td>-0.152485</td>\n      <td>-0.210863</td>\n      <td>-0.402720</td>\n      <td>-0.405765</td>\n      <td>0.188701</td>\n      <td>-0.097941</td>\n      <td>-0.297871</td>\n      <td>-0.396775</td>\n      <td>...</td>\n      <td>-0.138224</td>\n      <td>-0.256418</td>\n      <td>-0.251756</td>\n      <td>-0.223437</td>\n      <td>-0.070376</td>\n      <td>-0.177121</td>\n      <td>-0.346486</td>\n      <td>-0.436565</td>\n      <td>-0.355526</td>\n      <td>-0.266414</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.427679</td>\n      <td>0.514241</td>\n      <td>0.741948</td>\n      <td>0.342925</td>\n      <td>0.257924</td>\n      <td>0.103852</td>\n      <td>0.654724</td>\n      <td>0.615842</td>\n      <td>0.364539</td>\n      <td>0.220431</td>\n      <td>...</td>\n      <td>-0.026559</td>\n      <td>0.164448</td>\n      <td>0.602041</td>\n      <td>0.716180</td>\n      <td>0.006673</td>\n      <td>-0.042893</td>\n      <td>0.096039</td>\n      <td>0.396876</td>\n      <td>0.394738</td>\n      <td>0.593009</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>4.525668</td>\n      <td>3.983032</td>\n      <td>3.885003</td>\n      <td>5.553707</td>\n      <td>5.108024</td>\n      <td>8.140492</td>\n      <td>1.768746</td>\n      <td>3.211707</td>\n      <td>4.626660</td>\n      <td>5.485968</td>\n      <td>...</td>\n      <td>26.153357</td>\n      <td>14.471823</td>\n      <td>12.666717</td>\n      <td>11.989204</td>\n      <td>40.991026</td>\n      <td>27.866206</td>\n      <td>13.296703</td>\n      <td>10.486907</td>\n      <td>27.683404</td>\n      <td>8.905791</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 36 columns</p>\n</div>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 26
    }
   ],
   "source": [
    "pdFtsData = pd.DataFrame(data=X_train_g)\n",
    "pdFtsData.head(10)\n",
    "pdFtsData.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x720 with 36 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdFtsData.hist(figsize = (12,10))\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "outer range (low) of the distribution:\n[[-0.36236047]\n [-0.36189883]\n [-0.36182174]\n [-0.3614909 ]\n [-0.36149033]\n [-0.36126132]\n [-0.36099917]\n [-0.36082307]\n [-0.36070375]\n [-0.36062404]]\n\nouter range (high) of the distribution:\n[[ 4.06566099]\n [ 5.06067344]\n [ 5.20798425]\n [ 6.44982645]\n [ 6.95435173]\n [ 9.4408256 ]\n [13.46343005]\n [14.76815212]\n [17.66274869]\n [20.16956662]]\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "y_train_g_scaled = y_train_g\n",
    "y_train_g_scaled = preprocessing.StandardScaler().fit_transform(y_train_g_scaled)\n",
    "low_range = y_train_g_scaled[y_train_g_scaled[:,0].argsort()][:10]\n",
    "high_range= y_train_g_scaled[y_train_g_scaled[:,0].argsort()][-10:]\n",
    "print('outer range (low) of the distribution:')\n",
    "print(low_range)\n",
    "print('\\nouter range (high) of the distribution:')\n",
    "print(high_range)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stderr",
     "text": [
      "D:\\Users\\ASUS\\anaconda3\\envs\\deeplab\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n  warnings.warn(msg, FutureWarning)\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from scipy.stats import norm\n",
    "from scipy import stats\n",
    "\n",
    "chl_vals_g = pd.DataFrame(data=y_train_g,columns=['in_situ'])\n",
    "#histogram and normal probability plot\n",
    "sns.distplot(chl_vals_g['in_situ'], fit=norm);\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(chl_vals_g['in_situ'], plot=plt)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "name": "stderr",
     "text": [
      "D:\\Users\\ASUS\\anaconda3\\envs\\deeplab\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n  warnings.warn(msg, FutureWarning)\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chl_vals_g['in_situ'] = np.log(chl_vals_g['in_situ'])\n",
    "sns.distplot(chl_vals_g['in_situ'], fit=norm);\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(chl_vals_g['in_situ'], plot=plt)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1080x1080 with 2 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAy4AAANBCAYAAAAGEeokAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzde3hU5bn38d+akANJDAfNJERqEaHoFgY11QK7RbetgAgICrVEAcHSuLcBpQehntKtCGjtplR4W+WqFkQBQQlgEZGwpfgSq2ht1Hqoh2g1QoKo4ZCQw6z3D9+mxuAaxtwzs0i+n+vKdTEHfnPnmUNy53nWsxzXdV0BAAAAgI8FEl0AAAAAAERC4wIAAADA92hcAAAAAPgejQsAAAAA36NxAQAAAOB7NC4AAAAAfI/GBQAAAECbbdy4USNHjtSwYcP04IMPtrr9lVde0aWXXqoxY8aosLBQNTU1UeXTuAAAAABokz179mjhwoV66KGHVFJSotWrV+vNN99scZ/bb79dM2fO1IYNG3TyySfr97//fVSP0cmyYAAAAADtR01NzRFnRrKyspSVldV8eefOnRo0aJC6du0qSRo+fLg2b96soqKi5vuEw2EdPHhQklRbW6suXbpEVQuNCwAAAJAADXvfTnQJES1b+UctXry41fVFRUWaMWNG8+WqqiplZ2c3Xw4GgyovL2/xf+bMmaNp06Zp3rx56ty5sx5++OGoaqFxAQAAAHBEU6ZM0bhx41pd//nZFumz2RTHcZovu67b4nJdXZ1uvPFG/eEPf1AoFNL999+v2bNn69577z3qWmhcAAAAABzRF5eEfZnc3Fzt2rWr+XJ1dbWCwWDz5TfeeEOpqakKhUKSpMsuu0yLFi2KqhYOzgcAAADQJkOGDFFZWZn27dun2tpabdmyRUOHDm2+/etf/7p2796tt9/+bHlcaWmpBgwYENVjMOMCAAAAJEK4KdEVmMnJydGsWbM0efJkNTQ0aPz48QqFQpo+fbpmzpypAQMGaP78+bruuuvkuq6OP/54zZs3L6rHcFzXdWNUPwAAAIAv0VD190SXEFFysG+iS2jGUjEAAAAAvsdSMQAAACAR3HCiKzimMOMCAAAAwPdoXAAAAAD4HkvFAAAAgEQIs1QsGsy4AAAAAPA9GhcAAAAAvkfjAgAAAMD3OMYFAAAASACX7ZCjwowLAAAAAN+jcQEAAADgeywVAwAAABKB7ZCjwowLAAAAAN+jcQEAAADgeywVAwAAABKBXcWiwowLAAAAAN+jcQEAAADgeywVAwAAABIh3JToCo4pzLgAAAAA8D0aFwAAAAC+x1IxAAAAIBHYVSwqzLgAAAAA8D0aFwAAAAC+R+MCAAAAwPc4xgUAAABIhDDHuESDGRcAAAAAvkfjAgAAAMD3WCoGAAAAJIDLdshRYcYFAAAAgO/RuAAAAADwPZaKAQAAAInArmJRYcYFAAAAgO/RuAAAAADwPZaKAQAAAInArmJRYcYFAAAAgO/RuAAAAADwPZaKAQAAAIkQbkp0BccUZlwAAAAA+B6NCwAAAADfY6kYAAAAkAjsKhYVZlwAAAAA+B6NCwAAAADfo3EBAAAA4Hsc4wIAAAAkQphjXKLBjAsAAAAA36NxAQAAAOB7LBUDAAAAEoHtkKPCjAsAAAAA36NxAQAAAOB7LBUDAAAAEoFdxaLCjAsAAAAA36NxAQAAAOB7LBUDAAAAEsB1mxJdwjGFGRcAAAAAvkfjAgAAAMD3WCoGAAAAJAInoIwKMy4AAAAAfI/GBQAAAIDv0bgAAAAA8D2OcQEAAAASIcwxLtFgxgUAAACA79G4AAAAAPA9looBAAAAicB2yFFhxgUAAACA79G4AAAAAPA9looBAAAAiRBuSnQFxxRmXAAAAAD4XlxnXBr2vh3Ph/Pk1h00yzq8YLZJzo41WSY5rhyTnO899SOTnED2101y/Kbu1pkmOc+szjDJOe+V+SY5flQ7u9AkZ8djJ5jkbO9sEqPbd91uE2Tk08unmuT8+cU8k5zSzq5Jzo76SpMcSfq/zy4xyamecK1JzitvB01ydqYlmeS8LpufrQ2yOWD5juMOm+S8Xd3VJOe1lGSTnI8CNu8NSTo+bPM7Q7pRSb9ptPld8a+7d5rkwF9YKgYAAAAkAruKRYWlYgAAAAB8j8YFAAAAgO+xVAwAAABIhDBLxaLBjAsAAAAA36NxAQAAAOB7EZeKvfXWW3riiSe0e/duBQIBBYNBfec739GAAQPiUR8AAAAAeM+4PPjgg/rxj38sSRowYIBOP/10SdLNN9+s++67L/bVAQAAAO2VG/b/l494zrgsX75cJSUl6ty55dnWpk6dqnHjxmnatGkxLQ4AAAAApAgzLp06dVJjY2Or6+vq6pScbHP2VwAAAACIxHPG5eqrr9bYsWM1ePBgZWdny3EcVVVV6ZlnntGsWbPiVSMAAADQ/rAdclQ8G5fRo0frnHPOUVlZmaqqqhQOh/XNb35TM2bMUE5OTrxqBAAAANDBRdxVLCcnR2PHjo1HLQAAAABwRBEbFwAAAAAxwFKxqHACSgAAAAC+R+MCAAAAoM02btyokSNHatiwYXrwwQdb3f72229r0qRJGjNmjK666ip9+umnUeXTuAAAAAAJ4LpNvv86Wnv27NHChQv10EMPqaSkRKtXr9abb775ue/V1X/+539q+vTp2rBhg0477TTde++9UY1Xhz3GxUnLMMtK6vM1k5y8tA9Nchoak0xy3MMHTXLarfrW5zj6KpLkmuS0Z26jzRpgq7FO8tvffMJH/4PFi9UJkq3GOSDHJKeTY/OZKEmqrzWJsRprx2efH47Rc2b1HrMa52TZBNmMjpRqliSlGL2E0sM2QcmW71eYqKmpUU1NTavrs7KylJWV1Xx5586dGjRokLp27SpJGj58uDZv3qyioiJJ0iuvvKL09HQNHTpU0menXTlSrpcO27gAAAAA8LZs2TItXry41fVFRUWaMWNG8+WqqiplZ2c3Xw4GgyovL2++/N577+mEE07QDTfcoFdffVW9e/fWzTffHFUtNC4AAABAIhwDu4pNmTJF48aNa3X952dbJCkcDstx/jUb6Lpui8uNjY169tlntWLFCg0YMEC//vWvtWDBAi1YsOCoa6FxAQAAAHBEX1wS9mVyc3O1a9eu5svV1dUKBoPNl7Ozs/X1r39dAwYMkCSNGjVKM2fOjKoWny3UBgAAAHCsGTJkiMrKyrRv3z7V1tZqy5YtzcezSNKZZ56pffv26bXXXpMkbdu2TaeffnpUj8GMCwAAAJAIVjtI+EBOTo5mzZqlyZMnq6GhQePHj1coFNL06dM1c+ZMDRgwQEuWLNFNN92k2tpa5ebm6s4774zqMWhcAAAAALTZ6NGjNXr06BbXLV26tPnfAwcO1Nq1a79yPkvFAAAAAPgejQsAAAAA3/NcKlZZWen5n/Py8kyLAQAAADqMY2A7ZD/xbFwKCwtVUVGhYDAo1215RlTHcVRaWhrT4gAAAABAitC4rFy5UgUFBSouLlZ+fn68agIAAACAFjyPccnMzNTcuXNVUlISr3oAAACAjsEN+//LRyJuhxwKhRQKheJRCwAAAAAcEbuKAQAAAPA9TkAJAAAAJAK7ikWFGRcAAAAAvkfjAgAAAMD34rpUzK072OYMJy3DoBJbyRN/bJLzjcZfmuSEP/rYJMf9y59MchrffNEkRylpNjlNjSYx9R/UmuRkJKWb5DSWPmCSI0lK62yT01BvElNfafOcZQUaTHJOr7cZH7PnrNFmfA7XJJnkWI1z/wabcU5KzjXJkaTGrStNchrqjMY6yWasT6u3+XWgS7K/fkY3Ntp8BnVNO2yS07/OJMZUSsBmqVKw2wGTnO8e6mmSc8zw2a5dfseMCwAAAADfo3EBAAAA4HvsKgYAAAAkAruKRYUZFwAAAAC+R+MCAAAAwPdoXAAAAAD4XsTGZevWrXrggQf03nvvtbh+9erVMSsKAAAAaPfCYf9/+Yhn43LXXXdpxYoVqqio0MSJE7V+/frm21atWhXz4gAAAABAirCr2Pbt27Vu3Tp16tRJkyZN0rRp05SSkqILL7xQruvGq0YAAAAAHZxn4+K6rhzHkST16tVL99xzj6ZOnaru3bs3Xw8AAADgK3D9tRTL7zyXio0YMUKTJk1SeXm5JKlv375atGiRrrvuulbHvAAAAABArHjOuBQVFSk/P18ZGRnN1+Xn5+vRRx/VfffdF/PiAAAAAECK0LhI0uDBg1td16NHD914440xKQgAAADoEHy2a5ffcR4XAAAAAL5H4wIAAADA9yIuFQMAAAAQA+wqFhVmXAAAAAD4nuPG8UyS+4tGtjkjqc/XDCqRkif+2CRHkpSUbJdlwK3Za5Lz0eTZJjmfVnU2yamvTzLJCbs25yAKODZvnZSUJpOccJPduZXq6m0mY5vCNn8bSe5kM0adOzeY5KSkNprkvL+ni0lOk9FrumvaYZOczAybnNQMm3FOz7XJkaQ/P9vDJCcv7ZBJTlZWnUlO5y42743UbJu/HgdSbD47Kp45ziQn52v7TXLSehj93PhGtkmOJIUP2ryGkk7tbZJz+Mm/muR0e+Qpk5xYq11/Z6JLiKjzxdcnuoRmLBUDAAAAEoFdxaLCUjEAAAAAvkfjAgAAAMD3WCoGAAAAJAK7ikWFGRcAAAAAvkfjAgAAAMD3aFwAAAAA+F7EY1wqKirUuXNn5eTkaM2aNXr99dd11llnaeTItp+TBQAAAOiw2A45Kp6Nyx/+8Ac98MADCofDGjRokD788ENdcMEFeuSRR/TOO+/ommuuiVedAAAAADowz8blkUce0aZNm7R3716NGjVKzzzzjFJTUzVhwgSNHz+exgUAAABAXHg2LuFwWCkpKTrxxBM1bdo0paamNt/W1NQU8+IAAACAdoulYlHxPDh/2LBhuuKKK9TU1KQZM2ZIkl577TUVFBTowgsvjEuBAAAAAOA543LttdfqueeeU1JSUvN1KSkpmjFjhs4999yYFwcAAAAA0lHsKnb22We3uNy7d2/17t07ZgUBAAAAHYLrJrqCYwrncQEAAADgezQuAAAAAHwv4lIxAAAAADHArmJRiWvjsmNNVpsz8tI+NKhE+kbjL01yJCn5yhvMsiw4WSeY5JS+faJJzpvJNus3ax2bN3fAcUxyzq+12RL8tJxqk5wle3JNciTpYJLN9+Yk2Yz1qFqb19CAr9eY5GSckWmSc8t6f72mr6pLjXyno5DX51OTnIzv5JnkBL5tt5nMb6c9ZpLz83qbse7Z02as088/xSTHOWuQTU5ahklO4/9dY5KTfrLNApWU8840yXG+eb5JjiQF9lTY5PQKmeR0HmjzGkL7xFIxAAAAAL7HUjEAAAAgEVgqFhVmXAAAAAD4Ho0LAAAAAN+jcQEAAADgexzjAgAAACSCyzEu0WDGBQAAAIDvRdW4LFiwIFZ1AAAAAMCX+tKlYj//+c9bXbdt2zZ9+ulnJ7uaP39+7KoCAAAA2ju2Q47KlzYuXbt2VUlJia6++mplZX12xvtnnnlG55xzTtyKAwAAAADJY6nY7Nmz9T//8z/atGmT8vLyNG7cOHXp0kXjxo3TuHHj4lkjAAAAgA7Oc1exwYMH67TTTlNxcbGeeuopNTU1xasuAAAAoH1z3URXcEyJeHB+165dtWjRIvXu3VvZ2dnxqAkAAAAAWjjq87hMmDBBEyZMiGUtAAAAAHBEnIASAAAASAR2FYsKJ6AEAAAA4Hs0LgAAAAB8L65LxVw5bc5oaEwyqEQKf/SxSY4kuTV7TXKcrBNMcqyE2/50SZJSDZ53SQq4Nn22zSvIrut3jQY61TV6wiTJsRklu7G22dEw3Gg0RkZT++lm42z13NvsbuNarXwI29QT/tNTCnxrkElWumPzY9O1er9ajbVj9Ilm9uTbMBtnqw/847JMYgLdTzTJkaSw0XMW6J5nktNY+YZJzjGDpWJRYcYFANCuWTUtAIDEonEBAAAA4Hs0LgAAAAB8j+2QAQAAgETw2XFhfseMCwAAAADfo3EBAAAA4HssFQMAAAASwDXa9r2j8GxcysvLFQqFJEllZWXavn27OnXqpAsuuEADBw6MS4EAAAAA4LlUrLi4WJL04IMPat68ecrNzdUJJ5ygW265RStWrIhLgQAAAABwVEvFHn74YS1fvlzdunWTJI0fP17jx4/XFVdcEdPiAAAAgHYrzK5i0fCccWlsbFQ4HFbXrl2VkpLSfH1KSooCAY7rBwAAABAfnt1H165ddd555+mdd97RbbfdJumzY11+8IMfaMSIEXEpEAAAAAA8l4o98MADkqS3335bNTU1kj6bbZk5c6bOO++8mBcHAAAAtFucgDIqR3WMS+/evZv/nZ+fH7NiAAAAAOBIOFAFAAAAgO9xAkoAAAAgETgBZVSYcQEAAADge47runFr9Q6/+r9tznAPHzSoRHL/8ieTHEn65PfPmeSUvn2iSU7YMYnRxL/eahNkxD2wzyQn/MHrJjl1/+f3JjkbnsozybF8vsIf77bJ+fANk5y6RX8wyVn9bE+TnEe11yRnw5qpJjnugY9NcvbPX2mS8+gbX7PJMRpnSWoyOgD2sWXjTXI+vtlmrB9/z+bnxmOBT01yGmQzzmlOkknOL7vWmuRs35tjk5NsU0+G4YKZHq5N1glNNr98bAzYfJ499t4fTXJi7dCSokSXEFH6NYuP+r4bN27Ub3/7WzU2NmrKlCm6/PLLj3i/p556Srfeequ2bdsWVS0sFQMAtGtWTQsAmGtHJ6Dcs2ePFi5cqEcffVQpKSn6wQ9+oG9961vq06dPi/vt3btXd9xxx1d6DJaKAQAAADiimpoavf/++62+/nmqlH/auXOnBg0apK5duyo9PV3Dhw/X5s2bW+XddNNNKir6ajNNzLgAAAAAOKJly5Zp8eLWy8WKioo0Y8aM5stVVVXKzs5uvhwMBlVeXt7i/yxfvlz/9m//poEDB36lWmhcAAAAABzRlClTNG7cuFbXZ2VltbgcDoflOP861sl13RaX33jjDW3ZskV/+MMftHv3VzuWlsYFAAAASIRj4BiXrKysVk3KkeTm5mrXrl3Nl6urqxUMBpsvb968WdXV1br00kvV0NCgqqoqFRQU6KGHHjrqWjjGBQAAAECbDBkyRGVlZdq3b59qa2u1ZcsWDR06tPn2mTNn6oknntD69et17733KhgMRtW0SDQuAAAAANooJydHs2bN0uTJkzV27FiNGjVKoVBI06dP10svvWTyGBGXiu3YsUMDBw5UVlaWSkpKVF5ertNPP12XXnqpSQEAAABAhxS/0ynGxejRozV69OgW1y1durTV/Xr27Bn1OVykCDMut99+u+655x4dPnxYv/71r7Vhwwb16dNHTz75pObOnRv1gwEAAADAV+E547Jz505t2LBBSUlJ2r59u1avXq2UlBRddtllGjVqVLxqBAAAANDBeTYuaWlp+uijjxQMBpWbm6tDhw4pJSVFtbW16tSJDckAAACAr+wY2FXMTzy7j2uuuUbjx4/XRRddpJ49e2rSpEkaPHiwnn76af3whz+MV40AAAAAOjjPxuX8889X3759tXXrVr377rs644wzlJGRoQULFigUCsWrRgAAAAAdXMT1Xl/72tc0derUeNQCAAAAdBzh9rWrWKxxHhcAAAAAvkfjAgAAAMD32BoMAAAASASXXcWiEdfGJZD99Xg+nLee/6bGpx4yifq0qrNJzpvJNuscU+WY5PiNk9ndJCep32CbnOxVJjnjJnyqJ1ZntTln/YCbdfFLtxlUJAW65foqJ6m7zXs1s8kkRt2S00xykvqcbZJjJSltpUmO1ThP1gkqSa4xyXq59kOTnE79zzXJCXSyek3b/Nzo2inFJKei0eb5OiCrdf82Pw8zjI5DGHk4TU+n2rxB9qvRJOeQk2SSs9cmRm/WVtkEoV3qsEvFrJoWoK0smhZJZk0L4Bd+a1qAtvJb0wIcazps4wIAAADg2MExLgAAAEAisB1yVJhxAQAAAOB7NC4AAAAAfI+lYgAAAEACuGG2Q44GMy4AAAAAfM+zcZk7d64+/fTTeNUCAAAAAEfk2biUlJTo+9//vrZs2RKvegAAAICOIez6/8tHPBuXnj17asmSJVq+fLkmTJigTZs2qa6uLl61AQAAAICkCAfnO46jPn36aMWKFdq5c6dWr16t22+/Xb169VJubq5+9atfxatOAAAAAB2YZ+Piuv+aHhoyZIiGDBmihoYGvf766/rHP/4R8+IAAACAdstlV7FoeDYul19+eavrkpOT1b9/f/Xv3z9mRQEAAADA53ke4zJhwoR41QEAAAAAX4oTUAIAAACJ4LNdu/yOE1ACAAAA8D0aFwAAAAC+x1IxAAAAIBHC7CoWjY7buKSkmUXV1yeZ5NQ6Ni/egGszkeYe2GeS42R2N8nxm0CGzWsoM9xkktOeOZ1TTHKOM/oBkeOkmuT4TSDNMcnJNBrn443G+XBTvUmOJCkp2STGSbJZ12411t1k8329J5vXUL3RFrGOY1NPutVr2rX5feGQ47+fGwccm9f0gcZakxy0TywVAwAAAOB7NC4AAAAAfK/jLhUDAAAAEontkKPCjAsAAAAA36NxAQAAAOB7LBUDAAAAEsFoB72OImLjUlZWprS0NJ155pm677779Oyzz6p///760Y9+pJQUmy1KAQAAAMCLZ+Ny5513ateuXWpsbFTPnj3lOI4mTpyobdu26dZbb9XcuXPjVScAAACADsyzcdmxY4fWr1+v+vp6nXfeedqxY4eSk5M1dOhQXXzxxfGqEQAAAGh/2FUsKp4H57uuq/379+vjjz9WbW2tDhw4IEmqq6tTQ0NDXAoEAAAAAM8Zl+nTp2vYsGFyXVc/+9nPNG3aNA0ePFhlZWW69NJL41UjAAAAgA7Os3G5+OKLNXz4cDU1NSkjI0Nnn322nn76af30pz/Vv//7v8erRgAAAKDdccPsKhaNiLuKpaWlNf+7X79+6tevX0wLAgAAAIAv4gSUAAAAAHyPE1ACAAAAicCuYlFhxgUAAACA79G4AAAAAPC9jrtUrKnRLCrsOiY5AccmJ8kkRQp/8LpJTlK/wSY5vpNkM9LpTpNJTnvmpBiNtWsz1t3ddvrRafSnrHTXZpecrkY/opIDySY5lhyjsU6TzVh3M3pNd7Yaa6ONlpyATZDVOHdxbT7L9lq9gCQlG/0OU+vYjFGS4feG9qed/vQFAAAAfI5jXKJCWwsAAADA92hcAAAAAPgeS8UAAACARDA6JrCjYMYFAAAAgO/RuAAAAADwvYhLxbZu3aqtW7equrpaycnJOumkk3ThhRfqzDPPjEd9AAAAQPvErmJR8Zxxueeee/TII48oFArJcRydccYZysnJ0Q033KCHH344XjUCAAAA6OA8Z1w2bdqkkpISOY6jSy+9VNOnT9fy5cv1/e9/v/kLAAAAAGLNs3E5fPiwamtrlZ6errq6On3yySeSpPT0dAUCHB4DAAAAfFUuS8Wi4tm4XHLJJZo4caK+/e1v6+mnn9Yll1yiyspK/dd//ZdGjRoVrxoBAAAAdHCejcuPfvQjDRgwQH/72980Z84cDR48WAcPHtQdd9yhfv36xatGAAAAAB1cxF3FBg8erMGDBzdfzsjIoGkBAAAA2oqlYlHhQBUAAAAAvkfjAgAAAMD3aFwAAAAA+F7EY1wAAAAAxEA4nOgKjimO67pxOypo/0yDLZTrG9ueIan+g1qTHEn64KUsk5w9tekmOVbTaPnfqzbJScrOMMkJZKSZ5CgpySQmZeY8k5zXz5lpkvP1kXYffoEuNs+Z38b6tbOvNcn5pDbVJOes6UbvVscmx2qcX/2mzTjvq7V5z1d0snm+JGn8ZJufHak/+aVJjt9e09VKMclpchyTnIufm2OS89q3bzDJ2V9nM86funZ/d06Wza+BnwZsajoQsHnur/xghUlOrO0vGpnoEiI6bvGmRJfQjKViAAAAAHyPpWIAAABAIrAdclSYcQEAAADgezQuAAAAAHyPpWIAAABAIrBULCrMuAAAAADwPRoXAAAAAL7HUjEAAAAgAeJ4OsV2IWLjsmPHDm3evFm7d+9WIBBQMBjU0KFDNXz48HjUBwAAAADejcuiRYtUXl6uMWPGKBgMynVdVVdXa+3atXrxxRc1e/bseNUJAAAAoAPzbFw2bdqkxx9/XIFAy0NhRo0apVGjRtG4AAAAAF8Vu4pFxfPg/NTUVO3evbvV9ZWVlUpJSYlZUQAAAADweZ4zLnPmzNHll1+uXr16KTs7W47jqKqqShUVFZo/f368agQAAADQwXk2LkOGDNHmzZtVXl6uqqoqhcNh5ebmauDAgcy4AAAAAG3BUrGoeDYulZWVkqQTTzxRJ554YvP1e/fulSTl5eXFsDQAAAAA+Ixn41JYWKiKiormHcU+z3EclZaWxrQ4AAAAAJAiNC4rV65UQUGBiouLlZ+fH6+aAAAAAKAFx41wys7y8nKtWbNGt912W5sf7Mmcy9qckSSbtYAZSY0mOZLUJbPOJCezm02OG3ZMcv63sodJTrrR+s3McJNJTrpjk9M9vdYkp9+zvzHJ+d/TbzDJkaROCpvkJDs2Ocdn2Ix13z/fbZKzb8JUk5w3Xs02ybGSnXXIJOeUnYtNcvaNn2aSk9TZbg35q3+xec6CXQ+a5PR+eolJzkeX2Ix1INlmrB3PPU+P3sfvdzbJOXnH/zHJ+XDEdJOcJKNxlqSmBpvfGaykZ9v8fnbC49tNcmLt06nfS3QJEXW5f2uiS2jmOeMiSaFQSKFQKB61AAAAADhGbdy4Ub/97W/V2NioKVOm6PLLL29x+9atW3X33XfLdV317NlT8+fPV5cuXY463+hvGgAAAAA6qj179mjhwoV66KGHVFJSotWrV+vNN99svv3AgQP6xS9+oXvvvVcbNmxQv379dPfd0a2EiDjjAgAAACAGjoHtkGtqalRTU9Pq+qysLGVlZTVf3rlzpwYNGqSuXbtKkoYPH67NmzerqKhIktTQ0KDi4mLl5ORIkvr166eNGzdGVQuNCwAAAIAjWrZsmRYvbn3sYlFRkWbMmNF8uaqqStnZ/zWQOLEAACAASURBVDoOMBgMqry8vPlyt27ddMEFF0iS6urqdO+992rSpElR1ULjAgAAAOCIpkyZonHjxrW6/vOzLZIUDoflOP/a7MF13RaX/2n//v265pprdOqppx4x1wuNCwAAAJAINptvxtQXl4R9mdzcXO3atav5cnV1tYLBYIv7VFVV6aqrrtKgQYN0ww3R74bKwfkAAAAA2mTIkCEqKyvTvn37VFtbqy1btmjo0KHNtzc1Nenqq6/WhRdeqBtvvPGIszGRMOMCAAAAoE1ycnI0a9YsTZ48WQ0NDRo/frxCoZCmT5+umTNnavfu3frb3/6mpqYmPfHEE5Kk/v376/bbbz/qx6BxAQAAABLAPQZ2FYvG6NGjNXr06BbXLV26VJI0YMAAvfbaa23K92xcnnvuOc//fPbZZ7fpwQEAAADgaHg2LkuWLNGLL76oUCgk123ZETqOo+XLl8e0OAAAAACQIjQuS5cu1eTJkzVlyhR997vfjVdNAAAAQPvXzpaKxZrnrmLJycmaN2+e/vKXv8SrHgAAAABoJeJ2yCeffLJ++tOfxqMWAAAAADgiz6VilZWVnv85Ly/PtBgAAAAAOBLPxqWwsFAVFRUKBoNHPDi/tLQ0psUBAAAA7VY40QUcWzwbl5UrV6qgoEDFxcXKz8+PV00AAAAA0ILjfnEq5QvKy8u1Zs0a3XbbbW1+sIa9b7c5w0pj6QNmWe/c8KxJzgNNXUxyUl3HJOfG59v+nLdndb8oMskpW5NlkvMfr8wzyfGj2tmFJjlb/5htkvNAyn6TnIdfWGSSY6VmylSTnG0v9DTJWZb8iUnOiwfeNcmRpHfe2GCS89El00xynnnDZsn2itQDJjlvHf7IJMfKutx0k5wXd9t8djyWVm+S84lrkyNJuU5nk5zjlGSS82DNSyY57+2zyYm1Ty77j0SXEFHX1f+b6BKaec64SFIoFFIoFIpHLQAAAECH4bIdclQi7ioGAAAAAIlG4wIAAADA9yIuFQMAAAAQA+wqFhVmXAAAAAD4Ho0LAAAAAN9jqRgAAACQAOwqFh1mXAAAAAD4Ho0LAAAAAN/zbFwaGxu1bNkyLViwQLt27Wpx29133x3TwgAAAIB2LXwMfPmIZ+Nyyy236NVXX1UwGNT111+v3/3ud823bdu2LebFAQAAAIAU4eD8l19+WRs2bJAkjR07VldeeaXS0tJ05ZVXynU5mAgAAABAfHg2Lq7r6tChQ0pPT1f37t21dOlSTZw4Ud27d5fjOPGqEQAAAGh3XJ8txfI7z6ViV1xxhcaNG6eysjJJUk5OjpYuXaqFCxfqrbfeikuBAAAAAOA543LZZZfpW9/6llJSUpqvO+WUU7Rx40atXbs25sUBAAAAgBShcamsrGxuWiorK1vcNmzYsNhVBQAAAACf49m4FBYWqqKiQsFgsNXB+I7jqLS0NKbFAQAAAO0Wx7hExbNxWblypQoKClRcXKz8/Px41RQfaZ3NourqPYfxqB1MajLJkZNkEhP+eLdJTqBbrkmO3wS6ZJjkdOJTKyLnuDSTnDSjoyB7BOw+P/wkkG6z6YrVOGcHbJ73hqZGkxxLgWSbnTmTjXb4DDo2Y/1uwObnT2PY5udhoJPNa9FqnI9XsknOftm9pq3ORL5fNs/ZgYY6kxy0T56v18zMTM2dO1clJSXxqgcAAAAAWok4VRAKhRQKheJRCwAAANBhsB1ydKxmCAEAAAAgZmhcAAAAAPiezVHlAAAAAKLDUrGoMOMCAAAAwPdoXAAAAAD4HkvFAAAAgARgV7HoMOMCAAAAwPcizrjs3LlTxx13nE477TTdfffdev3115Wfn69p06YpKcnmDLkAAAAA4MWzcfnlL3+pF154QQcOHFAwGNTxxx+viRMnavPmzZo3b55uvvnmeNUJAAAAtCssFYuOZ+Oyfft2bdy4UZ988okuuOACPfvsswoEAho6dKjGjh0brxoBAAAAdHARj3Gpr69Xt27dNHv2bAUCn9394MGDamxsjHlxAAAAACBFaFwKCgo0ZswYNTU1acKECZKkF154QWPGjNGUKVPiUiAAAAAAeC4VKygo0NChQ1schJ+Xl6d77rlHffv2jXlxAAAAQHvFMS7R8WxcKisrFQgEVFlZ2eL6jIwMVVZWKi8vL6bFAQAAAIAUoXEpLCxURUWFgsGgXNdtcZvjOCotLY1pcQAAAAAgRWhcVq5cqYKCAhUXFys/Pz9eNcVHQ71ZVFPY5jyeTpJjkmN1dp3wh2+Y5AS65Zrk+I7ReYySHTa6iMTpZPMeS5Ub+U5HoXvkU2Adk5yAzWdQqmszzt2UbJKTmmSTY8kxOv1zimzWmVi9pjMDqSY5tWowybFiNc7dXJvXYrVj9xmUZnQu8kNGY5SS1D4/X7+Ua/O521F4vlozMzM1d+5clZSUxKseAAAAAGglYlsbCoUUCoXiUQsAAAAAHFEHm48DAAAA/IFdxaJjtMoWAAAAAGKHxgUAAACA77FUDAAAAEgAN8yuYtFgxgUAAACA79G4AAAAAPA9looBAAAACcCuYtGJesblxz/+cSzqAAAAAIAv5TnjMmnSJDlOy4OGXn75ZU2ePFmStHz58thVBgAAAAD/n2fjMnz4cC1dulTXXnutevbsKdd1dfPNN6uoqChe9QEAAACAd+NyxRVXaNCgQSouLtaECRM0duxYZWRk6JxzzolXfQAAAEC75LpshxyNiMe49OnTR/fff79ee+01zZw5U/X19fGoCwAAAACaHdXB+SkpKZozZ44uu+wyDRw4MNY1AQAAAEALnkvFKisrW1w++eSTVVRU1Hx9Xl5e7CoDAAAA2jG2Q46OZ+NSWFioiooKBYNBua4rSXIcR67rynEclZaWxqVIAAAAAB2b4/6zIzmCAwcOqKCgQMXFxcrPz2/zg9VcdUGbM9xGm9a0vrLRJEeSPniji0lOdV1nk5yoT87zJc769h6TnKTuKSY5TmejnJQkk5zUOQtNcv7+rRkmOT3Pszv+zDkuzSank82r0WqsXz9npklOTW2qSU7oCn/9qS119q9Mcl47+1qTnE+MxlmS3gvYvKYvLthvkmP1mvbbWO+Tzed0k0mKNPrluSY5VuNs9dnxqWt3/vBkfemvgVH5JGBTU71jc7D65ZUrTHJi7f1vnZ/oEiLq+edtiS6hmedvFZmZmZo7d65KSkriVQ8AAKasmhYAsOaGHd9/+UnE9jgUCikUCsWjFgAAAAA4IqtVRQAAAAAQM3aLJAEAAAActS8/0hxHwowLAAAAAN+jcQEAAADgeywVAwAAABLAb7t2+R0zLgAAAAB8j8YFAAAAgO95LhXbunWrvve970mS1qxZoz/96U/q1KmTLrjgAo0cOTIuBQIAAADtEUvFouM547JkyRJJ0t13363HHntMF198sUaOHKlHH31UCxcujEuBAAAAAHBUB+c/+eSTWrNmjVJTUyVJ5513nkaNGqVZs2bFtDgAAAAAkCLMuBw6dEh79+5Vbm6uDhw40Hx9XV2dOnViQzIAAAAA8eHZuJx11lmaOnWqXnjhBf3iF7+QJG3ZskVjxozRFVdcEY/6AAAAgHbJdf3/5See0ybz58+X9NkMS3V1tSSpV69e+t3vfqd+/frFvjoAAAAAUITGpbKysvnfSUlJqqysVGZmZvNteXl5sa0OAAAAABShcSksLFRFRYWCwaDcL8wVOY6j0tLSmBYHAAAAtFdshxwdx/1iR/I5Bw4cUEFBgYqLi5Wfn9/mB/tjzsQ2ZyTJZrFdVqDBJEeSju9yyCSnS7DWJCfcaPMm2PCPE01yMptMYnRcOGySk+7aFJSbbvO8n/rcIpOcjf1vMsmRpDTXZqxTjd6vORkHTXL6Pfsbk5yaqVNNcl54Ntckx+pMwnlZ+01y+pQtNsmxGmdJCqTZfC7u+lOOSc7XutiM9Sk7jcZ6is1YOykmMWaqX003yen99BKTnI8umWaSE0i2O/Cg6bDNJ4jRjw11PtHme+u25imTnFh7e8CwRJcQUe+XtiS6hGaer9bMzEzNnTtXJSUl8aoHAABTVk0LACCxIu5pHAqFFAqF4lELAAAA0GG4Ln9YiYbVCgMAAAAAHdjGjRs1cuRIDRs2TA8++GCr21999VVdcsklGj58uG688UY1NjZGlU/jAgAAAKBN9uzZo4ULF+qhhx5SSUmJVq9erTfffLPFfX72s5/plltu0RNPPCHXdfXwww9H9Rg0LgAAAEACuGH/f9XU1Oj9999v9VVTU9Pie9m5c6cGDRqkrl27Kj09XcOHD9fmzZubb//ggw9UV1enM844Q5J0ySWXtLj9aEQ8xgUAAABAx7Rs2TItXtx6B8OioiLNmDGj+XJVVZWys7ObLweDQZWXl3/p7dnZ2dqzZ09UtdC4AAAAADiiKVOmaNy4ca2uz8rKanE5HA7Lcf612YDrui0uR7r9aNC4AAAAAAkQPgZ2FcvKymrVpBxJbm6udu3a1Xy5urpawWCwxe3V1dXNl/fu3dvi9qPBMS4AAAAA2mTIkCEqKyvTvn37VFtbqy1btmjo0KHNt5944olKTU3V888/L0lav359i9uPhmfj0tjYqFWrVumjjz5SfX29Fi9erMLCQv3mN7/R4cOHv8K3BAAAAKC9ycnJ0axZszR58mSNHTtWo0aNUigU0vTp0/XSSy9Jku666y7Nnz9fI0aM0KFDhzR58uSoHsNzqdjs2bMlScOHD9cdd9yhQ4cOqaCgQE899ZRuuOEG/epXv/qK3xoAAACA9mT06NEaPXp0i+uWLl3a/O9TTz1Va9eu/cr5no3LG2+8oY0bN0qSnn/+ea1bt06O4+jcc8/VyJEjv/KDAgAAAB2dewwc4+InnkvF0tPT9fe//12S1Lt3b3344YeSPjvBTEpKSuyrAwAAAABFmHGZM2eOpk6dqrPOOkudO3fW97//fQ0cOFCvvPKK/vu//zteNQIAAADo4DwblzPPPFObN2/Wzp079e677+rkk0/WCSecoJtvvlm5ubnxqhEAAABod9wwS8Wi4dm4VFZWSpL69++v/v37N18fDodVWVmpvLy82FYHAAAAAIrQuBQWFqqiokLBYFCu60qSHMdpPtNlaWlpXIoEAAAA0LF5Ni4rV65UQUGBiouLlZ+f3+YH2965zRFKMjtnZqpOr7fJ6pFaY5KTcUamSY7CYZOYR/+x1ySnW3KaSU6Ok2qS0931fNkfte/W2tSzb8JUk5wHUiKf1fZo9QgYvFkldff+iDlqI43GumaqzVhn3X+/Sc7Wb95okpNq9Lk4ti7ZJGf/Vf4aZ0lq3Px7k5xtz71jkjOxPskkx2ysl9mMdcOjd5vkqKHBJCb88tsmOVafHcc/aveabnjwTpOc8LuVJjkK2Cx5Sjq9n0nOseL/zwvgKHn+tMvMzNTcuXNVUlISr3rixqppAQD4m1XTAviFVdMCHGsi/jk0FAopFArFoxYAAAAAOCKbdRwAAAAAosKuYtFhvRQAAAAA36NxAQAAAOB7LBUDAAAAEiDsslQsGsy4AAAAAPA9GhcAAAAAvsdSMQAAACABXJaKRcVzxuXqq6/WP/7xj3jVAgAAAABH5Nm4/PWvf9VVV12l++67Tw0NDfGqCQAAAABa8GxccnJy9NBDD+m1117TsGHDdO+99+qDDz6IV20AAAAAICnCMS6O4+iEE07QnXfeqYqKCj388MOaNm2aDh8+rNzcXK1atSpedQIAAADtiusmuoJji2fj4n5uNHv16qXrr79e119/vT7++GOOfQEAAAAQN56Ny6xZs454fbdu3dStW7eYFAQAAAAAX+TZuPTt21eVlZVfenteXp55QQAAAEBHEGY75Kh4Ni6FhYWqqKhQMBhssWxM+uz4l9LS0pgWBwAAAABShMZl5cqVKigoUHFxsfLz8+NVEwAAAAC04LhfnEr5gvLycq1Zs0a33XZbmx+sYe/bbc6w0lj6gFnWrmtfMsm5NzVskpPuJJnkLFo13iQnqc/ZJjl+U79ojknOC0ttnvdBL99pkuNHh+/4iUlO2fIUk5ytnT13kj9qc3fdbpJj5dCs6SY5T28JmuSUdG40yXmo6jmTHEn65L1tJjk1U6aa5Dyzy2bJ9mOdm0xySg/a/JyvazpsklPWL9skZ9c7uSY5T6XZfN6XN+4zyZGk7kmdTXIaXZvv7fGqv5rk1NW9Z5ITa3856eJElxDRme+tT3QJzTxnXCQpFAopFArFoxYAAAAAOCKbPxsCAAAAQAxFnHEBAAAAYI8TUEaHGRcAAAAAvkfjAgAAAMD3WCoGAAAAJAAnoIwOMy4AAAAAfI/GBQAAAIDveTYujY2NWrt2rR577DE1NDTo1ltv1ejRo/Xzn/9cn3zySbxqBAAAANDBeTYuN910k/70pz/pscce06RJk9SpUyctXLhQvXr10i233BKvGgEAAIB2x3Ud33/5iefB+a+88oo2btyopqYmnXvuuVq1apUkqU+fPrr44ovjUiAAAAAAeDYugUBA77zzjvbv36/9+/fr/fffV8+ePbVv3z41NjbGq0YAAAAAHZxn4/Kzn/1MU6dOVTgc1q9+9StNnz5d3/jGN/TSSy9p5syZ8aoRAAAAaHfYDjk6no3Lt7/9bT311FPNl8844wzt2rVLM2fO1CmnnBLr2gAAAABAUoTGpbKystV1oVCo+ba8vLzYVAUAAAAAn+PZuBQWFqqiokLBYFCu67a4zXEclZaWxrQ4AAAAoL1yI98Fn+PZuKxcuVIFBQUqLi5Wfn5+vGoCAAAAgBY8G5fMzEzNnTtXa9as8U/jEm6yyTHcFa3J6MCqgGOTkySbHPfAxyY57ZbjeRqkKISNchCJ1TOWapbUPiUZ/Q0xXUkmOalJySY5fmQ11p2NXtOdk1JMclyf/R26k2v1mjYa54Dda/o4xyZrvxpMcpICfL7iy3k2LtJnx7T887gWAAAAADbYVSw6tLUAAAAAfI/GBQAAAIDvRVwqBgAAAMCey1KxqDDjAgAAAMD3aFwAAAAA+B6NCwAAAADf4xgXAAAAIAE4k1t0PBuXcDis5cuXq7S0VNXV1UpOTtZJJ52kkSNH6qKLLopXjQAAAAA6OM/GZcGCBWpoaNAPf/hDPfHEEzr11FMVDAa1YsUKVVRU6JprrolXnQAAAAA6MM/G5ZlnntGGDRskSd/5znd0+eWXa+XKlTr//PM1ZswYGhcAAADgK3LFdsjR8Dw4v6mpSR999JEkqbq6WnV1dZKkhoYGderE4TEAAAAA4sOz+7jqqqt0ySWX6Mwzz9Rf//pX/eQnP9G7776rK6+8UkVFRfGqEQAAAEAH59m4/LNpef3113XdddepV69eqq+v17p169S1a9d41QgAAAC0O2E30RUcWzwbl8rKSqWmpioUCjVf/qdDhw4pLy8vttUBAAAAgCI0LoWFhaqoqFAwGJTrtmwJHcdRaWlpTIsDAAAAAClC47Jy5UoVFBSouLhY+fn58aoJAAAAaPfC7CoWFcf94lTKF5SXl2vNmjW67bbb2vxge4ef2+YM1+gUo4drkmyCJO3dnWmSU3M41STHyqmnV5nkJKWZxCiQZvTm9txL7+hl/vY+k5y3hthsdJHd96BJjiQF0m3G2gnY5GQs+b1JzpuDbca6ti7ZJOeU8w6Y5FhJX7jUJOfv35phknOwLsUkp7rR7rP134dVm+T4baz319qMUU2TzXvDaovY/3hlnknO6+fMNMmxek0fMBpnSUoyOnf7p45NTclGv+iN3LPKJCfWtuV8P9ElRHT+nocTXUKziHsah0Kh5mNcAAAAACAROBkLAAAAkACcgDI6RotmAAAAACB2aFwAAAAA+B5LxQAAAIAEMNpzqsNgxgUAAACA79G4AAAAAPC9iEvFduzYoc2bN2v37t0KBAIKBoMaOnSohg8fHo/6AAAAAMC7cVm0aJHKy8s1ZswYBYNBua6r6upqrV27Vi+++KJmz54drzoBAACAdoXtkKPj2bhs2rRJjz/+uAKBlivKRo0apVGjRtG4AAAAAIgLz2NcUlNTtXv37lbXV1ZWKiUlJWZFAQAAAMDnec64zJkzR5dffrl69eql7OxsOY6jqqoqVVRUaP78+fGqEQAAAGh32A45Op6Ny5AhQ/TTn/5U77zzjpKSktSzZ0/l5uZq4MCBWrdunQYNGhSvOgEAAAB0YJ5Lxe666y6tXbtWH3/8sR544AGFw2GdffbZSklJ0apVq+JVIwAAAIAOznPGZfv27Vq3bp06deqkSZMmadq0aUpJSdGFF14o13XjVSMAAADQ7rBULDqejYvrunKcz7Zp69Wrl+655x5NnTpV3bt3b74eAAAAAGLNc6nYiBEjNGnSJJWXl0uS+vbtq0WLFum6667Te++9F5cCAQAAAMBxI6z5KisrUzAY1CmnnNJ83Ycffqj77rtPN954Y1QP9seciV+tys9Jks0StaxAg0mOJAW7HTDJyepRZ5LjGs07rnvzayY5mU0mMcoM23xj6UYDlNv5oEnOabsWmeSUDLjZJEeS0ozGKNVoSWmPdJuxPvU5m7Hef9VUk5yyZ/JMcqw+F0/K3G+S0/fPd5vk7J9uM85JmZ6LC6Ly9JagSU7vrE9NcvqULTbJsRrrQHqSSY6VPc+nmuT0fnqJSc4nE43GOc0kRpLUdMgmx+p3j7SeNu/XLg+UmuTEmsXvxrF20Z6ViS6hWcRXx+DBg1td16NHj6ibFgAAAAD4qjyXigEAAACAH9jNnwMAAAA4amH2uooKMy4AAAAAzLiuqzvuuEMjRozQyJEj9fzzzx/xfosXL9ZFF12kiy66SHfeeWfEXBoXAAAAAGaeeOIJvfXWW9q0aZOWLFmin//852psbGxxn507d+rpp5/WunXrVFJSoldeeUVPPvmkZy5LxQAAAAAcUU1NjWpqalpdn5WVpaysrCP+n+3bt2vkyJEKBAI6+eST1aNHD/3lL3/R2Wef3Xyf7OxszZkzRykpKZKkU045RZWVlZ610LgAAAAACRCW/w9yWbZsmRYvbr31elFRkWbMmHHE/1NVVaVg8F/bx2dnZ2v37t0t7tO3b9/mf1dUVOjxxx/XypXeWy/TuAAAAAA4oilTpmjcuHGtrv+y2RZJCofDcpx/NWWu6yoQOPIRKn//+99VWFio66+/Xr169fKsxbNxee655zz/8+enewAAAAC0L15Lwj5v0aJF2rZtmyRp//79qqqqar5t7969LWZg/un555/XzJkzdcMNN+iiiy6K+BiejcuSJUv04osvKhQKyf3CGbAdx9Hy5csjPgAAAACA1tzIdzlmXHvttbr22mslSY8//rgeeeQRjRo1Su+//74qKio0YMCAFvf/8MMPdc0112jhwoVHPOH9kXg2LkuXLtXkyZM1ZcoUffe73/2K3wYAAACAjmLEiBEqLy/XmDFjJEm333670tLStGfPHv3oRz/S+vXr9fvf/16HDx/WggULmv/fD37wA02cOPFLcz0bl+TkZM2bN0+PPPIIjQsAAACAiBzH0ezZszV79uwW1+fk5Gj9+vWSpJtuukk33XRTVLkRz+Py1ltvKScnR++9916L61evXh3VAwEAAAD4l/Ax8OUnno3LXXfdpRUrVqiiokITJ05s7pAkadWqVTEvDgAAAACkCEvFtm/frnXr1qlTp06aNGmSpk2bppSUFF144YWtDtYHAAAAgFjxbFxc123eg7lXr1665557NHXqVHXv3r3F3swAAAAAohPm9+moeC4VGzFihCZNmqTy8nJJn53hctGiRbruuutaHfMCAAAAALHiOeNSVFSk/Px8ZWRkNF+Xn5+vRx99VPfdd1/UD1baue3LywKy6Uz7N3Q2yZGkr2V8YpKT8Z08kxyFbZbxPfrmXpOcrOQUk5zjnVSTnK7eL/ujdmFtmknOvvHTTHKWJWdEvtNRyg7YfG/dlGySM67W5rmvmTrVJCfr/vtNckq+OTvynY5CupJMcq6os3mv7p9uM87HLbUZ58bnN5nkSNKGHdtNcq4+bPPeOFBo8/lhNtY7HzXJUWO9SUxD2U6TnAP/aTPOXVcajfO2FSY5khR+6y2THKeTzeeQc1p/kxy0TxF/gzvSCWF69OihG2+8MSYFAQAAAB0BR4xHJ+J2yAAAAACQaDQuAAAAAHzPZrE/AAAAgKj47QSPfseMCwAAAADfo3EBAAAA4Hs0LgAAAAB8j2NcAAAAgAQI25yesMPwnHFpbGzUsmXLtGDBAu3atavFbXfffXdMCwMAAACAf/JsXG655Ra9+uqrCgaDuv766/W73/2u+bZt27bFvDgAAAAAkCIsFXv55Ze1YcMGSdLYsWN15ZVXKi0tTVdeeaVcl3N9AgAAAF9VWKwVi4Zn4+K6rg4dOqT09HR1795dS5cu1cSJE9W9e3c5DgMNAAAAID48l4pdccUVGjdunMrKyiRJOTk5Wrp0qRYuXKi33norLgUCAAAAgOeMy2WXXaaamhq98MILOvHEE3XSSSfplFNO0cb/x969x0VV538cfw/ggDc20RDSdYm0rTW1ZDWxVs31BpK3tMQghbLchKy21FK77GZl2bauumWWKZppBIgaWqFJFzRvpdmmbSaWiRdMVxCRy5zfHz2aX6gNM/oFRnw9e/B4xGF4z2fOHGfmw/d7vmfFCi1evLimagQAAADqHE688IzLEZfp06frk08+0Y8//qjY2FhlZmZKkho1aqRVq1bVSIEAAAAA4HLEJScnR8uWLZOvr6/i4+OVmJgou92uqKgoTs4HAAAAUGOqPDn/Z2FhYZozZ44SEhI4OR8AAAA4T1yA0jMuG5d+/fopPj5eEydOVPv27dWmTRvNmDFDSUlJKi0t9fjOPirdf86F/szP5nveGZLkWy/ESI4k3RxSbiTH58buRnJ0qsRITMW8LCM5O07mG8k5VeH5MXc29XzqGcm52n6VkZxr6psZvfy8aK+RHEkqqzBzTPv7mtnXEQEdSBI5EAAAIABJREFUjORcE2DmHaJ89WtGchYf2mQkx9R+7tsgwkjOlY1cvrW4rXyLmdcgv4hoIzmS9MahF4zk3B74RyM5PoGG9vW2bCM5fl2HGMkxpaz8UyM5fk0DjOSUb11tJMevZ5yRHEkqb7jcSI4tqLmRHN821xvJQd3k8hUvKSlJERERatiwoXNbRESE0tPTNW/evGovDgAAAACkKhoXSYqMjDxjW2hoqCZNmlQtBQEAAAAXA0dtF3CBcbmqGAAAAAB4AxoXAAAAAF6PxgUAAACA1zOzHAkAAAAAj3BVRM8w4gIAAADA69G4AAAAAPB6VU4Vy83NVePGjXX11Vdr5syZ2rVrlyIiIpSYmChfXzMXgwQAAAAuNg4z10W+aLhsXJ5//nlt3bpVRUVFCg4OVtOmTRUbG6vVq1fr6aef1pQpU2qqTgAAAAAXMZeNS05OjlasWKFjx46pd+/e2rhxo3x8fNStWzcNGjSopmoEAAAAcJGrcqpYaWmpmjRpogkTJsjH56dTYk6cOKHy8vJqLw4AAACoqxy1XcAFxuXJ+SNGjNCAAQNUUVGhYcOGSZK2bt2qAQMGaOTIkTVSIAAAAABU2bjExsZq8eLF+u677yRJl112mebMmSPLYuVpAAAAADXDZeMyffp0rVu3Tnl5eYqNjVVmZqZCQkLUpk0bLVmypKZqBAAAAOocxwXw5U2qPDk/IyNDfn5+io+PV2Jioux2u6KiohhxAQAAAFBjXDYulmXJZvtpgemwsDDNmTNHCQkJCgoKcm4HAAAAgOpms1wMncyaNUu5ubmaOHGi2rdvL0nasmWLkpKSVFpaqi1btnh0Z6X7vji/aiWp9OT5Z0gqz37TSI4krZuSbyTnJf8iIzkNbFUuFueW+fOijeT4XdPdSI5865nJMeTUCw8byfnsdTN/BOiy4zkjOd7o1LMPGMlZvzDASM7a+mYuvvu3zU8ZyTGl+IHRRnI+fi/YSM7y+mZWr3zj0GYjOZJ09Ls1RnKOJyQYycnddJmRnMyAMiM57x3fZSSnqKzESM6Odi2N5OTuMbOf1/ib2c+bSw8YyZGk5n6NjeSccJQayfno0H+M5JSX/mAkp7q9/Nu42i6hSmO+X1TbJTi5/ISblJSkiIgINWzY0LktIiJC6enpmjdvXrUXBwAAAACSG9dxiYyMPGNbaGioJk2aVC0FAQAAAMDpXK4qBgAAAADewMzJEAAAAAA84m3LDXs7RlwAAAAAeD0aFwAAAABej6liAAAAQC1gqphnGHEBAAAA4PU8blwefPDB6qgDAAAAAH6Vy6li8fHxstkqX9V7x44duuOOOyRJKSkp1VcZAAAAUIdZtV3ABcZl49K3b1/NnTtX48aNU8uWLWVZlqZMmaKkpKSaqg8AAAAAXE8Vi4uL02uvvaa0tDTt379f119/vRo2bKjOnTurc+fONVUjAAAAgItclee4tG7dWq+//rp27typ++67T6WlpTVRFwAAAFCnOWze/+VNqlwOOTs7W/n5+RoxYoT27dunlStXSpKWLl2q2267rdoLBAAAAACXIy7Tp0/XokWLlJeXp9jYWB0+fFhPP/20JGnJkiU1UiAAAAAAuBxxycnJUUZGhvz8/BQfH6/ExETZ7XZFRUXJslgHAQAAADhXXIDSMy4bF8uynMshh4WFac6cOUpISFBQUNAZyyQDAAAAQHWxWS6GTmbNmqXc3FxNnDhR7du3lyRt2bJFSUlJKi0t1ZYtWzy6sx8ie55ftZIsQ61pWYmvmSBJ/ztW30hOcWk9IzmWZaapvDz8iJEcHz8zo3M2X0M5Hl929eyaLH3dSM63N441kvOb4JNGciTJp5537etL3jSzr3d3NbOUe2mpmdePFm2PG8kxJXCBmf38TaSZ/VxyysxrYmGp3UiOJLXtdMhITuDr3rWvT5aY2dcnygy9j8nM+1jkjmlGcv57fbKRnBMlZo7FE+VVnqLsNl+bmdf7QstMTY1sFUZyuh1INZJT3V5sFVfbJVTpge8W1XYJTi6PsqSkJEVERKhhw4bObREREUpPT9e8efOqvTgAAACgrmKqmGeqbI8jIyPP2BYaGqpJkyZVS0EAAAAAcDpDEzkAAAAAoPrQuAAAAADweubO7gIAAADgNi4u4hlGXAAAAAB4PRoXAAAAAF6PqWIAAABALXBwPXePuGxcsrOz1atXL0lSamqqPvzwQ/n5+al3796Kjo6ukQIBAAAAwOVUsdmzZ0uSZs6cqZUrV2rgwIGKjo5Wenq6XnzxxRopEAAAAADcmir2/vvvKzU1Vf7+/pKkHj16KCYmRg888EC1FgcAAADUVY7aLuAC43LEpbi4WAUFBQoJCVFRUZFze0lJifz8OD0GAAAAQM1w2bh07NhRCQkJ2rp1q5544glJ0nvvvacBAwYoLi6uJuoDAAAAANdTxZ555hllZ2dr//796tatmyQpLCxML7/8sj7//PMaKRAAAACoi7gApWdcjrhMnz5db7zxhvbu3avbb79dmZmZuvLKK/X73/9eS5YsqakaAQAAAFzkXI645OTkKCMjQ35+foqPj1diYqLsdruioqJkWfSIAAAAAGqGy8bFsizZbD9dGScsLExz5sxRQkKCgoKCnNsBAAAAeM7BZDGP2CwXQyezZs1Sbm6uJk6cqPbt20uStmzZoqSkJJWWlmrLli0e3dl7zYefX7WSbIae4EDfMiM5knRp06Kqb+SGwJanjOSYWlsv88vfGslpVGHmOWvkMPPAAgztoOAGxUZyrto0w0jOO9dMNpIjSfUMjajaDe3rEC/b14V3JhjJWb/hMiM5voZeF1s1KjSS0+bTmUZyiu5JNJIjST6BZlbC/HDVpUZyWjf+n5mc9bOM5BT9xcy+9g2sZyTHlP0fmannilwz+/n4SDOvHb6NXM7090hFkZnXacvQZ496IWaes9+8nm0kp7pN/d3ttV1ClSbtfaO2S3By+UqelJSkiIgINWzY0LktIiJC6enpmjdvXrUXBwDA+TLVtAAAaleVr+aRkZFnbAsNDdWkSZOqpSAAAAAAOB1/hgIAAABqgaEZdhcNc5MkAQAAAKCa0LgAAAAA8Ho0LgAAAEAtsC6Ar3N6XJaladOmqV+/foqOjq5yJeJp06Zp4sSJVebSuAAAAAAw5t1339Xu3buVlZWl2bNn65FHHlF5eflZb7t+/XplZGS4lcvJ+QAAAADO6vjx4zp+/PgZ2wMDAxUYGHjW38nJyVF0dLR8fHx0+eWXKzQ0VJ999pk6depU6XbHjh3Tiy++qDFjxmjnzp1V1uKycSkvL9fbb7+t3r17q3HjxnrllVf0xRdfqG3btrrnnnvk7+9f5R0AAAAAONOFsKrYggULNGvWmRdhTUpKUnJy8ll/59ChQwoODnZ+f+mll+rAgQNn3O6xxx7TAw88oPz8fLdqcdm4TJgwQZLUt29fTZs2TcXFxRoxYoTWrVunRx99VC+88IJbdwIAAADgwjNy5EgNHjz4jO2/NtoiSQ6HQzabzfm9ZVny8al8hkpqaqpCQ0MVGRmp9PR0t2px2bh8/fXXWrFihSRpy5YtysjIkM1mU/fu3RUdHe3WHQAAAAC4MLmaEvZLM2bM0Nq1ayVJhYWFOnTokPNnBQUFlUZgJCkrK0uHDx/WwIED9b///U/FxcV6+umn9eijj/7qfbhsXBo0aKD//ve/atOmjcLDw5Wfn6/LLrtMBw8elN1ur/IBAAAAADg7h63q21woxo0bp3HjxkmSVq1apbS0NMXExGjfvn3Ky8tTu3btKt3+9ddfd/5/enq6Nm7c6LJpkapoXCZOnKiEhAR17NhR9evX16233qoOHTroyy+/1JNPPnmujwsAAABAHdWvXz9t375dAwYMkCRNnTpVAQEBOnjwoO6++25lZmaeU67LxuW6667TQw89pH379ikgIECXX365mjVrpilTpignJ+ec7hAAAABA3WWz2TRhwgTn+fI/a968+VmbliFDhmjIkCFV5rq8jsv06dOVmZmpo0ePat68eQoNDdXQoUMVEhKiJUuWePgQAAAAAPzMIcvrv7yJyxGXnJwcZWRkyM/PT/Hx8UpMTJTdbldUVJQsy7seCAAAAIC6y2XjYlmWcymzsLAwzZkzRwkJCQoKCqq0xBkAAAAAVCeXjUu/fv0UHx+viRMnqn379mrTpo1mzJihpKQklZaWenxnuQG+51yoaVeXunzoHmn1m6NGchr0vMJIjmwuZwC6beVXB43kXOJnZgW6JqpnJscy89z3OGnmAqxHhiQayVnkX/VShe4KtgUYyQly/RLjtr6G9vXxkQlGcgIXvF71jdyw8o8TjeTUdz3r1223GdrPhaPN7OfGc83sZ0kq35ZtJCfzg/eM5NxbYub1rOgvZl4/Gr00z0hO+X8+NJKj0hIjMWVr3zGScyL5TiM5pl47JHPHtLX7KyM5Nh9Df9Bu1dpMzgWC+UuecfmpIikpSREREWrYsKFzW0REhNLT0zVvnpkXOQAAqpOpD3iAt+CYxsWqyj+HRkZGnrEtNDRUkyZNqpaCAAAAAOB0ZuYXAAAAAEA1MneiBwAAAAC3OWq7gAsMIy4AAAAAvB6NCwAAAACvx1QxAAAAoBZ425XpvR0jLgAAAAC8nsvGZcyYMfr+++9rqhYAAAAAOCuXjcu2bdt05513at68eSorK6upmgAAAIA6z7oAvryJy8alefPmWrx4sXbu3Kk+ffrolVde0Q8//FBTtQEAAACApCpOzrfZbGrWrJmee+455eXl6a233lJiYqJOnTqlkJAQLVmypKbqBAAAAHARc9m4WNb/DxCFhYVp/PjxGj9+vI4ePcq5LwAAAMB54AKUnnE5VeyBBx5Qdna2Fi5cqO+++865vUmTJvrqq6+qvTgAAAAAkKpoXDZu3KhFixYpLy9PsbGxyszMdP6MaWIAAAAAaorLqWIffvihMjIy5Ofnp/j4eCUmJsputysqKqrSNDIAAAAAnuEClJ6p8hwXm80m6adzXObMmaOEhAQFBQU5twMAAABAdXM5Vaxfv36Kj4/X9u3bJUlt2rTRjBkzdP/991c65wUAAAAAqpPLEZekpCRFRESoYcOGzm0RERFKT0/XvHnzPL6zXTrheYWnscnMSM9v6jWs+kZu8r/UzJoQto5djOTIMlNPmVYYyckrP24k5ztDz319n3pGctrqN0Zyfl/PzDDx7lNHjORI0l4fXyM5jXz8jeR00qVGcmx2IzEqS59pJGfNiW+N5NT3NfPAoqzfGsnxaWDm+CnPTTeS49d1iJEcSXrvuJnnfqT990ZyfAPNvJ6V/+dDIzl+f+hmJMeUsvJVRnJ8m9Y3klOxa72RHL8OvYzkSFJF4yZmguxm9pFvyz8YyUHd5LJxkaTIyMgztoWGhmrSpEnVUhAAAABwMeAMF8+4nCoGAAAAAN6AxgUAAACA16tyqhgAAAAA88yclXzxYMQFAAAAgNejcQEAAADg9ZgqBgAAANQCi3XFPOJyxKW8vFxvv/22Vq5cqbKyMv3tb3/TzTffrEceeUTHjh2rqRoBAAAAXORcNi6TJ0/Whx9+qJUrVyo+Pl5+fn568cUXFRYWpscee6ymagQAAABwkXM5VezLL7/UihUrVFFRoe7du2vJkiWSpNatW2vgwIE1UiAAAABQF7GqmGdcNi4+Pj7as2ePCgsLVVhYqH379qlly5b68ccfVV5eXlM1AgAAALjIuWxcHn74YSUkJMjhcOiFF17Q6NGjdeWVV+qLL77QfffdV1M1AgAAALjIuWxcbrzxRk2ePFn79+/XlVdeqYULF2rz5s267777tHnz5pqqEQAAAKhzHKwq5hGXJ+dPnz5db7zxhvbu3avY2Fh98skn6tevn6644grn+S4AAAAAUN1cjrjk5OQoIyNDfn5+io+PV2Jioux2u6KiomRZdIgAAAAAaobLxsWyLNlsNklSWFiY5syZo4SEBAUFBTm3AwAAAEB1czlVrF+/foqPj9f27dslSW3atNGMGTN0//3367vvvquRAgEAAIC6yLoAvryJyxGXpKQkRUREqGHDhs5tERERSk9P17x58zy+szIDq1X7uu61aoWP3UxNtoCGVd+oBgXYfI3kFBk67EstQ6udG4qpMDTqaPO+Q1rljgojOSdVZiTHTDUGlZl5XCUVp4zkWIb+jVk+XjaSXl5a2xWcoaisxEiOZfeyfV1q5nHBNav0ZG2XcAbf8AgjOdbJQiM5gCsuGxdJioyMPGNbaGioJk2aVC0FAQAAAMDpqmxcAAAAAJjHcsie8cJJKgAAAABQGY0LAAAAAK/HVDEAAACgFhhaL+iiwYgLAAAAAK9H4wIAAADA67mcKuZwOJSSkqI1a9bo8OHDqlevnlq1aqXo6Gj179+/pmoEAAAA6hxT1+G6WLhsXJ599lmVlZXprrvu0rvvvqurrrpKwcHBWrRokfLy8jR27NiaqhMAAADARcxl47JhwwYtX75ckvSnP/1Jt99+u95880317NlTAwYMoHEBAAAAUCNcNi4VFRU6cuSImjZtqsOHD6ukpESSVFZWJj8/FiQDAAAAzhWrinnGZfdx55136pZbbtG1116rbdu26a9//av27t2rUaNGKSkpqaZqBAAAAHCRc9m4DBkyREeOHFFpaanuv/9+hYWFqbS0VOnp6XrvvfdqqkYAAAAAFzmXyyFPnz5dn3zyiX788UfdfvvtyszMlN1uV5MmTbRkyZKaqhEAAACoc6wL4D9v4nLEJScnR8uWLZOvr6/i4+OVmJgou92uqKgoWZZ3PRAAAAAAdZfLxuWXzUlYWJjmzJmjhIQEBQUFyWazVXtxAAAAACBVMVWsX79+io+P1/bt2yVJbdq00YwZM3T//ffru+++q5ECAQAAAMBmVTHna/369QoODtYVV1zh3Jafn6958+Zp0qRJHt3Z7mv6nluVv2AZWjeuvNzXTJCkklNmloYur3DZR7rNssyMhjVrVmQkxxRTg3w2HzPTHEOXv2gkJ6/3w0Zy7P4VRnIkycfPuxZoDF0910jOtzeaufaUo8LMwRjYrMRIjinBK141kmNqP5eVmnmdLjP4et+85XEjOZcuf81Izu6uZlb4LCszta/NvI+Z0u4zM6/T30Sa2c+nSs18XigpM3dJClPzZ4oq6hnJCfI387p43XeZRnKq28iwW2q7hCotyEur7RKcqjzyIyMjz9gWGhrqcdMCAAAAAOfKu/40AgAAAABnYW6sEQAAAIDbHKzS6xFGXAAAAAB4PRoXAAAAAF6PqWIAAABALWCimGcYcQEAAADg9aoccfnoo4+0evVqHThwQD4+PgoODla3bt3Ut+/5X5MFAAAAANzhsnGZMWOGtm/frgEDBig4OFiWZenw4cN6++239fnnn2vChAk1VScAAABQpziYLOYRl41LVlaWVq1aJR+fyjPKYmJiFBMTQ+MCAAAAoEa4PMfF399fBw4cOGP7/v37Zbfbq60oAAAAAPgllyMuEydO1O23366wsDBdeumlstlsOnz4sPbs2aNnnnmmpmoEAAAA6hyLqWIecdm4dO3aVS+99JIKCwt16NAh7dmzR/n5+Xr44YfVpUuXmqoRAAAAwEXOZeMyf/58LVy4UA6HQ126dFF+fr769u2rtLQ07dmzR2PHjq2pOgEAAABcxFw2LmlpacrKylJBQYFiYmK0YcMG+fv7a9iwYRo6dCiNCwAAAIAa4bJxcTgcstvtatGihRITE+Xv7+/8WUVFRbUXBwAAANRVjtou4ALjclWxPn36KC4uThUVFUpOTpYk7dy5UyNGjFBUVFSNFAgAAAAANsuyXC5nsGnTJnXq1Mn5/bfffqvvv/9e3bt39/jO3m9+m+cVnqaeod70koBTRnIkqflvC43kNLjcZR/pPkMxGdmhRnIaOsysmNHAYea5DzB0DF1av9hIztWbZxjJeeeayUZyJKme65cFt9kN7euQBmb29VWbzOzr4wkJRnI+3tTCSI6foefrdw3NvJb9fuO/jOQU/SXRSI5f0wAjOZL0bmZTIzl/aHDMSE6bT2cayTmRfKeRHN+m9Y3kmLLvXTM5rdfPMpJjbD83MXdMOwpLjeTY7GY+fNj8XU4Gclvjf68yklPdbvvdoNouoUpL9y6r7RKcqjw6ftm0SFJ4eLjCw8OrrSAAAADgYuBgOWSPGPrbPAAAAABUHxoXAAAAAF7PzERCAAAAAB6xmCrmEUZcAAAAAHg9GhcAAAAAXo/GBQAAAKgFjgvg61xYlqVp06apX79+io6O1pYtW856u7Vr12rIkCGKiorSU089VWUujQsAAAAAY959913t3r1bWVlZmj17th555BGVl5dXus3333+vxx9/XP/+97+1fPly/ec//1FOTo7LXJcn52/atMnlL59+jRcAAAAAdcfx48d1/PjxM7YHBgYqMDDwrL+Tk5Oj6Oho+fj46PLLL1doaKg+++yzSr3D+++/r+joaIWEhEiSXnzxRfn7+7usxWXjMnv2bH3++edq3769rNOuzGyz2ZSSkuIyHAAAAMDZnf752hstWLBAs2bNOmN7UlKSkpOTz/o7hw4dUnBwsPP7Sy+9VAcOHKh0m71796pevXoaM2aM8vPz1aNHD91///0ua3HZuMydO1d33HGHRo4cqT//+c8ugwAAAADULSNHjtTgwYPP2P5roy2S5HA4ZLPZnN9bliUfn8pnqFRUVGjz5s1auHChGjRooL/85S/KyMjQkCFDfjXXZeNSr149Pf3000pLS6NxAQAAAC4yrqaE/dKMGTO0du1aSVJhYaEOHTrk/FlBQUGlERhJatasmSIjIxUUFCRJ6tWrl7Zv3+6ycany5Hybzab4+HhJUmpqqp566illZWVVWTwAAACAX+eQ5fVf7ho3bpwyMzOVmZmphx9+WCtWrFBFRYX27t2rvLw8tWvXrtLtb7rpJn388cc6fvy4Kioq9NFHH6lt27Yu78PliMv8+fO1cOFCORwOdenSRfn5+erdu7fS0tK0Z88ejR071u0HAwAAAKDu69evn7Zv364BAwZIkqZOnaqAgAAdPHhQd999tzIzM9WhQwfdddddGjFihMrKynTDDTfolltucZnrsnFJS0tTVlaWCgoKFBMTow0bNsjf31/Dhg3T0KFDaVwAAAAAVGKz2TRhwgRNmDCh0vbmzZsrMzPT+f3QoUM1dOhQt3NdThVzOByy2+1q0aKFEhMTKy1RVlFR4fadAAAAAMD5cNm49OnTR3FxcaqoqHAud7Zz506NGDFCUVFRNVIgAAAAUBdV91XvTXx5E5dTxcaNG6dNmzbJ19fXuc1utys5OVndu3f3+M522ut5XuFpbFXfxC3XlBgKkvS70DMvynMu7D2uM5KjxlWv/OCOnJxtRnICdf7PuyQ1tXyrvpEbfmMo548lri+S5K78fqON5KwMaGIkR5KaGnrOmlhmcv500sy+PjIk0UhO0/TXjeSs++OjRnIaVL3OilualdiN5ByLTTCSc8mbZvZz+dbVRnIkac3qdUZyfmdoXx8faWZfBy4ws68rdq03kmOVnjSSc2rFciM5J5LvNJLTcOZrRnIcB/cYyZEkx9euLzbutsaXmMmpKK/6NrhouWxcJFW6wqUkhYeHKzw8vNoKAgAAAIDTVdm4AAAAADDP8mC5YbhxHRcAAAAAqG00LgAAAAC8HlPFAAAAgFrgyZXpwYgLAAAAgAsAjQsAAAAAr8dUMQAAAKAWWBZTxTzhcsSlvLxcCxYs0LPPPqvNmzdX+tnMmTOrtTAAAAAA+JnLxuWxxx7TV199peDgYI0fP14vv/yy82dr166t9uIAAAAAQKpiqtiOHTu0fPlySdKgQYM0atQoBQQEaNSoUQxtAQAAAOfBUdsFXGBcNi6WZam4uFgNGjRQUFCQ5s6dq9jYWAUFBclms9VUjQAAAAAuci6nisXFxWnw4MFav369JKl58+Z69dVX9eKLL2r37t01UiAAAAAAuBxxue2229S5c2cFBARIklJTU7Vr1y7df//9Onr0aI0UCAAAAAAuG5f58+dr4cKFcjgc6tKli/Lz89W7d28tX75c1113XU3VCAAAANQ5ljhn3BMuG5e0tDRlZWWpoKBAMTEx2rBhg/z9/TVs2DANHTpUSUlJNVUnAAAAgIuYy3NcHA6H7Ha7WrRoocTERPn7+zt/VlFRUe3FAQAAAIBUxYhLnz59FBcXp5SUFCUnJ0uSdu7cqcmTJysqKsrjOzvic/7DYf7yvtXM7FdeaiTH9seeRnJ8gloYyWmoL43kFKrcSE6xzUyzXGBz2a+7rY3l8p+P28LqmRkmPmaVGsmRzD1nh21m9lF7Q/vax9C+LnvjOSM528t/NJJT36eekZyeFY2M5PgEGIlR+dpFRnL8esYZyZGkzaVLjOTcVtHcSI5vIzOvZ+Xbso3k+HXoZSTHlJKyLCM5vk3MHNSOg3uM5Pg0v9xIjiTZGjUxk9PwEiM5FxsHU8U84vLTwLhx47Rp0yb5+vo6t9ntdiUnJ6t79+7VXhwAAAAASFU0LpLUqVOnSt+Hh4crPDy82goCAAAAgNOZmX8BAAAAwCOWxVQxT5iZHAsAAAAA1YjGBQAAAIDXY6oYAAAAUAtYVcwzjLgAAAAA8Ho0LgAAAAC8XpVTxXJzc9W4cWNdffXVmjlzpnbt2qWIiAglJiZWur4LAAAAAPdZTBXziMvG5fnnn9fWrVtVVFSk4OBgNW3aVLGxsVq9erWefvppTZkypabqBAAAAHARc9m45OTkaMWKFTp27Jh69+6tjRs3ysfHR926ddOgQYNqqkYAAAAAF7kqz3EpLS1VkyZNNGHCBPn4/HTzEydOqLy8vNqLAwAAAACpisZlxIgRGjBggCoqKjRs2DBJ0tatWzVw4ECNHDmyRgoEAAAA6iKHZXn9lzdxOVVsxIgRioyMVEFBgZo3b67U1FRt27ZNw4YN06233lpTNQIAAAC8x2okAAAgAElEQVS4yLlsXObPn6+FCxfK4XCoS5cuys/PV+/evbV27VpVVFRo7NixNVUnAAAAgIuYy8YlLS1NWVlZKigoUExMjDZs2CB/f38NGzZMQ4cOpXEBAAAAzpF3TcTyfi7PcXE4HLLb7WrRooUSExPl7+/v/FlFRUW1FwcAAAAAUhUjLn369FFcXJxSUlKUnJwsSdq5c6cmT56sqKgoj++sqcN2blX+gt1Qa2r3cZgJkuQ4UWokx+dgnpEch2XmsYVaVV6f1C3FNu+6UGk96/yPQ0mqJzMr61WUmaknxFbfSI7kxnKDbgowlFRPZo7pilNm6nHs3W8kJ8jXzHPW2FbPSI6vqf1cbCRGjt27jeSUN1xuJEeSmvs1NpLj6zDzZlZRZOY5s3Z/ZSSnonETIzm+4RFGcsy8ukqOQjPv846vNxnJsTUys58lydbwEmNZJlR8t8NITr1m4UZy4F1cfjIdN26cNm3aJF/f///gabfblZycrO7du1d7cQAAAEBd5WCymEeq/JN6p06dKn0fHh6u8HC6WAAAAAA1x9SMEAAAAACoNmZOYgAAAADgEaaKeYYRFwAAAABej8YFAAAAgNdjqhgAAABQCyyLqWKeYMQFAAAAgNfzuHF58MEHq6MOAAAAAPhVLqeKxcfHy2arfN3ZHTt26I477pAkpaSkVF9lAAAAQB3GqmKecdm49O3bV3PnztW4cePUsmVLWZalKVOmKCkpqabqAwAAAADXU8Xi4uL02muvKS0tTfv379f111+vhg0bqnPnzurcuXNN1QgAAADgIlflOS6tW7fW66+/rp07d+q+++5TaWlpTdQFAAAAAE5VNi55eXk6evSoJk6cqBtuuEEdOnRQVlZWTdQGAAAA1FnWBfCfN3F5jsv8+fO1cOFCORwOdenSRQcOHFCvXr2UlpamPXv2aOzYsTVVJwAAAICLmMvGJS0tTVlZWSooKFBMTIw2bNggf39/DRs2TEOHDqVxAQAAAFAjXDYuDodDdrtdLVq0UGJiovz9/Z0/q6ioqPbiAAAAgLrKsrxrKpa3c9m49OnTR3FxcUpJSVFycrIkaefOnZo8ebKioqI8vrMGBp6bBg4zT3BwkyIjOZLke9VVRnJ8wtqbyQm6zEhOswpb1TdyQ4GvkRgV2cw89ydtDiM5//Nx+c+nxjWWoR0tqVBm/jBRLDP7+pihfW2ZKUfyMfNvo9xQQYUqM5LzP1tDIzmm9rPNz8wxbQtqbiRHkk44zCxQU2g1NpJjbF8bOqZlr28kxjpZaCSnqKKekRyb3ePrdZ9d40uMxNgamsnxRjZDxxDqJpefBsaNG6dNmzbJ1/f/3zzsdruSk5PVvXv3ai8OAAAAAKQqGhdJ6tSpU6Xvw8PDFR4eXm0FAQAAABcDh5et2uXtDI19AgAAAED1oXEBAAAA4PW86+xiAAAA4CLBqmKeYcQFAAAAgNejcQEAAADg9ZgqBgAAANQCVhXzjMvGJTs7W7169ZIkpaam6sMPP5Sfn5969+6t6OjoGikQAAAAAFxOFZs9e7YkaebMmVq5cqUGDhyo6Ohopaen68UXX6yRAgEAAADArali77//vlJTU+Xv7y9J6tGjh2JiYvTAAw9Ua3EAAAAAIFXRuBQXF6ugoEAhISEqKipyNi4lJSXy8+P0GAAAAOBcWZzj4hGXU8U6duyohIQEbd26VU888YQk6b333tOAAQMUFxdXE/UBAAAAgOsRl2eeeUZ79uxR/fr1VVZWptTUVL3zzjsaNmyYYmNja6pGAAAAABc5l43L/PnztXDhQjkcDnXp0kX5+fnq3bu31q5dq9mzZ2vs2LE1VScAAABQpzgspop5wmXjkpaWpqysLBUUFCgmJkYbNmyQv7+/hg0bpqFDh9K4AAAAAKgRLs9xcTgcstvtatGihRITE50n50tSRUVFtRcHAAAAAFIVIy59+vRRXFycUlJSlJycLEnauXOnJk+erKioKI/v7F/l355blb9Qz+Z73hmS9OfilkZyJGni+9uM5NTv0MVITvn+r43krPA5aiTnm5OHjOQUlZ80kuNrc9mvu+3qgPZGchpcWm4k541vvzCSI0lFZSVGcuy+ZlYfjGwQYSSnfgszQ/K+bX9vJGfVoSVGcnx9zBzTCY3NvAYFtDTzvNuuvsZIjnUkX35dBhnJ+ujQQ0ZyngzqaiSnXkg9Izlq1dpIjG/LPxjJMSXI38xrmc3f0EqqFWZe702q+G6HkRybvb6RHJ+QK4zkXChYVcwzLv8ljhs3Tps2bZKv7/83C3a7XcnJyerevXu1FwcAwPky1bQAAGpXlX9C6NSpU6Xvw8PDFR4eXm0FAQAAAMDpuIokAAAAUAtYVcwzZiZGAwAAAEA1onEBAAAA4PWYKgYAAADUAlYV8wwjLgAAAAC8Ho0LAAAAAK/nVuMyderUM7Y98sgjxosBAAAALhYOy/L6r3NhWZamTZumfv36KTo6Wlu2bDnr7TIzM9W/f3/1799f06ZNqzLX5TkuU6ZM0Q8//KBt27Zp9+7dzu1lZWU6duyYhw8BAAAAQF337rvvavfu3crKytLevXt1zz33KCsrS35+/996nDx5UlOnTtXq1asVGBio2NhY5ebmqmvXrr+a67JxGT16tPbt26epU6dq9OjRzu2+vr5q3bq1gYcFAAAAwFsdP35cx48fP2N7YGCgAgMDz/o7OTk5io6Olo+Pjy6//HKFhobqs88+q3Rh+4qKCjkcDp08eVINGjRQeXm5/P39XdbisnFp1aqVWrVqpXfeeUf5+fn65ptv1LVrVx06dEhBQUHuPFYAAAAAF6gFCxZo1qxZZ2xPSkpScnLyWX/n0KFDCg4Odn5/6aWX6sCBA5Vu06hRI40bN05RUVGqX7++OnXqpI4dO7qsxa3lkFevXq1Zs2appKRES5cu1S233KJHH31UMTEx7vw6AAAAgNNcCMshjxw5UoMHDz5j+6+NtkiSw+GQzWZzfm9Zlnx8Kp9av3PnTqWlpemDDz5Q48aN9dBDD+m1117TXXfd9au5bp2c/8orr2jJkiVq1KiRmjZtqoyMDL388svu/CoAAACAC1RgYKBatmx5xtfpjcuMGTM0cOBADRw4UHv37tWhQ4ecPysoKKg0AiNJH3/8sSIjI9W0aVPZ7XYNGTJEGzdudFmLW42LzWZTo0aNnN83b968UhcFAAAA4OI1btw4ZWZmKjMzUw8//LBWrFihiooK7d27V3l5eWrXrl2l21911VXKzc1VcXGxLMvS2rVrz7jN6dyaKta6dWu9+eabKi8v19dff63FixfryiuvPPdHBgAAAFzkznW5YW/Xr18/bd++XQMGDJD006VVAgICdPDgQd19993KzMzUjTfeqP/85z8aMmSI6tWrp3bt2unuu+92metW4/LYY49p1qxZ8vPz01//+ld16dJFTz755Pk/KgAAAAB1is1m04QJEzRhwoRK25s3b67MzEzn93fffXeVzcovudW4NGzY8Iw7BgAAAICaYrOsqseoevTooYKCAjVs2FCSdOLECTVs2FBhYWF68sknddVVV1V7oQAAAEBdEt7sutouoUrfFnxW2yU4uTXi0rlzZ/Xs2VP9+vWTJK1du1bZ2dkaPny4nnzySb355pvVWiQAAACAi5tbq4rt2rXL2bRIUs+ePbVz5061b99eJSUl1VYcAAAAAEhuNi6NGzdWamqqTp06pZKSEqWmpiowMFB5eXlyOBzVXSMAAABQ51iWw+u/vIlb57jk5+fr73//u9avXy9fX19FRkZq8uTJWr16tX73u9+pR48eNVAqAAAAUHdc3rRDbZdQpT1HttV2CU5unePy9ttv69///vcZ20eOHGm8IAAAAAA4nVtTxd5///3qrgMAAAC4qDhkef2XN3FrxKVJkyaKjo5W27ZtFRAQ4Nz+97//vdoKAwAAAICfudW4xMTEVHcdAAAAAPCr3Gpchg0bpsLCQp08eVKWZcnhcGjfvn3VXRsAAAAASHKzcZk9e7Zee+01lZeXKzAwUEeOHNHVV1+t9PT06q4PAAAAqJPcWNwXv+BW45KWlqZ169bpmWee0b333quvv/5ab7311nnf+e7du/Xuu+/qwIED8vHxUXBwsP70pz+pXbt25519LrKzs5Wfn6/u3burVatWzu1Lly7Vbbfd5lFWXl6e6tevr+bNmys1NVW7du1Sx44dFR0dfc71Pfvss5o4caLHv7d9+3a1b99ekrR+/Xrl5OTIz89PvXv3VocOni3D99FHH6lDhw4KDAzUsmXLtH37drVt21a33HKL2xlPPfWUkpOT9Zvf/Maj+z6b9evXKyAgQNddd53mzZunjRs36pprrtHdd98tu93udk52drays7N1+PBh1atXT61atVJUVJSuu+66864RAAAA58/3iSeeeKKqG2VlZSkuLk579+5VWVmZevXqpVdeeUUjRow45zt+44039I9//EOtW7fW5ZdfruDgYBUXF+vVV19VcXFxjX9gnD59urKzs+Xj46Np06apWbNmuuqqqyRJU6ZM0fDhw93Omj9/vv7+9787G5atW7cqIiJCWVlZ2rdvnzp37lxlxiOPPKI1a9ZU+lq1apW+/fZbrVmzRr169XK7njFjxmj48OF644039NJLL6lz585q0KCBXnrpJVVUVDibmqpMnTpVq1evVq9evTR37lx9+OGH+uMf/6gPPvhAn3/+ubp16+ZWzoMPPqh33nlHzZs31xVXXOH24zjdc889p/T0dK1bt06ffPKJDh48qFtvvVVffvml1q1bp549e7qVM2fOHH3wwQfq3Lmz8vLy1LFjRwUGBmrmzJmy2+1q27btOdcIXOg++ugjvfLKK1q8eLFWrlypTz/9VOXl5WrdunWN11JeXq6FCxdq9erVCggI0GWXXeb82cyZM3X99de7nZWbm6tjx46padOmmjFjhhYsWKD8/Hxde+218vFxa8FNo7KzsxUeHi5JSk1N1csvv6z3339fDodDbdq0cTunvLxcb731lkJDQ+Xn56eXXnpJr732mvbs2aNrr71Wfn5u/b1SY8aMUYcOHYz8gcnbcEzXDFPH9M+mTp16xueMRx55xKPPQ97oxWkv1XYJVXpgwr21XYKTW69gjRo10ooVK/SHP/xBb775pkJCQlRSUnJed5ySkqJly5apfv36lbYnJCRo8ODBSkxMdDtr//79Ln/+yxeCX5OTk6OMjAz5+fkpPj5eiYmJstvtioqK8ngYLy0tTVlZWSooKFBMTIw2bNggf39/DRs2TEOHDtXYsWOrzLjkkku0bNkyjRkzRoGBgZKkDRs2uNX0/Jq33npLKSkpatKkiSRp6NChGjp0qOLi4tz6/dzcXC1fvly+vr7KycnR0qVLZbfbddttt3m0gEPLli01ffp0PfHEE5o7d64SEhLUs2fPSivWueOjjz5SZmamSktL1aNHD3300UeqV6+eunXrpoEDB7qdk5WVpWXLlslms+mWW27R6NGjlZKSoltvvdX55Wldq1evrjSS2K1bN/Xt29ejHBPKy8v1xhtvKD8/X7169dIf//hH589mzpyp5ORkt7Nyc3PVuHFjXX311Zo5c6Z27dqliIgIJSYmytfXtzrK/1XZ2dnON6vU1FR9+OGHzlFET0c1y8vL9fbbb6t3795q3LixXnnlFX3xxRdq27at7rnnHvn7+7uVM2bMGE2aNEm//e1vPX483mrGjBnavn27BgwYoODgYFmWpcOHD+vtt9/W559/rgkTJriVs2nTJpc/79Spk1s5jz32mBwOh6688kqNHz9et956q8aMGSNJWrt2rdvH8/PPP6+tW7eqqKhIwcHBatq0qWJjY7V69Wo9/fTTmjJlils5Z/Pggw/qH//4h8e/N3v2bPXq1UszZ87U5s2bFR8fL8uytHTpUu3atUsPPPCAWzk/Pyd9+/bVtGnTVFxcrBEjRmjdunV69NFH9cILL7iVs23bNt15550aPny44uPjVa9ePY8fk/TTv69ly5YpICBAffv21TPPPKNNmzbpmmuu0YQJE3TJJZe4leNwOJSSkqI1a9ZUGhmPjo5W//793a6HY/rcnMtxbeqYnjJlin744Qdt27ZNu3fvdm4vKyvTsWPHPKrJG3nbcsPezmXjkpGRocGDB2vq1Klavny5br75Zq1Zs0YTJ07Ufffdd3537Oen8vLyM7aXlJR4/AJ5zz33KC8vz/ki9Es2m01r1qypMsOyLNlsNklSWFiY5syZo4SEBAUFBTm3u8vhcMhut6tFixZKTEys9MGnoqLCrYwJEyaoW7du+uc//6kHH3xQ119/vRYsWKDBgwd7VIv00xuHw+HQJZdcUmn6lN1u9+ivMAEBATpy5IiCg4MVEhKi4uJi2e12nTx50u2/4kk/PSetW7fWokWLlJubq6VLl2rq1KkKCwtTSEiI22+slmWpsLBQxcXFOnnypIqKitSkSROVlJSorKzM7XpOnTqlkydPqkGDBiopKXG+EDZo0MDjv1Lxpui52nxDlPig50pWVpZWrVp1xr+DmJgYxcTEuH08z549W59//rnat29/1tfolJQUt3J27Nih5cuXS5IGDRqkUaNGKSAgQKNGjfLoD0w5OTlasWKFjh07pt69e2vjxo3y8fFRt27dNGjQILdz4uPjz3h/2LFjh+644w5Jcvtx/dL777+v1NRU5/tGjx49FBMT4/Yx/fXXX2vFihWSpC1btigjI0M2m03du3f3qKlv3ry5Xn31VT333HPq06ePYmNj1b9/f7Vo0cKjxzN58mQVFxertLRUixYtUvv27fXiiy9qzZo1euyxx/Svf/3LrZxnn31WZWVluuuuu/Tuu+/qqquuUnBwsBYtWqS8vDy3/iAocUy7w/Rxfb7H9OjRo7Vv3z5NnTpVo0ePdm739fWtlVEy1C6XnzZTUlI0ePBghYSE6O6775b004uQCWPGjNGgQYMUGRmpSy+9VDabTYcOHdKGDRs8+tAhSW+++aZGjBihxx9/XBEREedUT79+/RQfH6+JEyeqffv2atOmjWbMmKGkpCSVlpZ6lNWnTx/FxcUpJSXF+cFw586dmjx5sqKiotzOiYyM1NVXX63HH39c69atc7vpOd0ll1yiHj16SPrp2jvPPvus1q9fr+eff179+vVzO2fs2LEaOnSo+vfvr5YtWyo+Pl6RkZH6+OOPddddd7md88sX465du6pr164qKyvTrl279P3337udM3r0aPXp00eWZenhhx9WYmKiIiMjtX79eo/OuRkyZIhiY2N144036uOPP9aQIUO0f/9+3XvvvR4vBc6bomve9oYo8UHPFX9/fx04cOCMUev9+/d7dA7Z3Llzdccdd2jkyJH685//7Pbvnc6yLBUXF6tBgwYKCgrS3LlzFRsbe05/YCotLVWTJk00YcIE57/XEydOnPUPar+mb9++mjt3rsaNG6eWLVvKsixNmTJFSUlJHtUiScXFxSooKFBISIiKioqcx3RJSYlHfxhq0KCB/vvf/6pNmzYKDw9Xfn6+LrvsMh08eNCj58xms6lZs2Z67rnnlJeXp7feekuJiYk6deqUQkJCtGTJErdyvvzyS61YsUIVFRXq3r278/dat27t0cj4hg0bnK9lf/rTn3T77bfrzTffVM+ePTVgwAC3GxeO6aqZOq5NHdOtWrVSq1at9M477yg/P1/ffPONunbtqkOHDikoKMijmlAHWC4MGjTI1Y/P24EDB6yMjAxrzpw51ksvvWRlZGRYBw4cOKesbdu2WZMnTz6venJzc61vvvmm0rb9+/dbTz31lMdZGzdurPT97t27rXXr1p1zbW+99ZaVkJBwzr//cw2fffaZZVmWtXnzZuuDDz7wOOO7776z5s2bZz3++OPW5MmTrX/+85/Wtm3bPMp46623PL7fX3Py5EmrqKjIsizL2rlzp/Xqq69aH3/8scc5ubm51quvvmrl5uZalmVZRUVF1s6dOz3Oufnmm60ffvjhjO3fffedNWDAALdzSktLreHDh1vZ2dke1/BLMTEx1okTJ5zfHzhwwLrpppuszMxMj/599+/f3yopKbEsq/LzV1hYaEVHR7uds3DhQqtbt25WWlqa9emnn1obNmywevfubX366afWp59+6nZOnz59rMOHD1ujR4+2CgoKnNuPHz/uUT2WZVm33nqr9fXXX1uWZVkPPPCA8/k7cOCANXDgQLdzfrk/9+zZY02bNs3q06eP1b17d+u2225zOycmJsayLMsqLy+3brjhhko/8+QYuvnmm53/X1FRYQ0fPtyyLMs6deqU1bdvX7cyPvnkE6tHjx7WqFGjrIcfftgaP368NWrUKKtHjx7W+vXr3a7Fsizr22+/tZ5//nmPfud0S5Yssfr06eP8d2pZlvXNN99YPXr0sNq1a+d2zhtvvGH16dPHKi8vd27bsmWLddNNN1lLly71qKb//ve/1ogRI6yMjAzLss79fXPixIlWTEyMFRERYSUlJVmWZVnvvvuu1aNHD2vx4sVu52zdutW64YYbrOTkZGv8+PHWDTfcYN17771W9+7dPXoP+rVj/8cff/ToNX/AgAHWt99+a23bts1q37699f3331uWZVlHjhzx6N9qdHS089/6gQMHnPu5qKjI6t+/v9s5ZzumExISvOqY3r17t7FjumfPnh4f05Zl5rg2dUz/bNWqVVb//v2tP//5z1ZBQYEVGRlprVixwuMcb3PZJX/w+i9vYrOsX/+z6zXXXKPmzZufrdlxewoWcDHJzc3VpEmTFBYWVmkkMS8vT88884y6dOnidtaePXuUlpamhx566JzrWbp0qebNm6cnnnhCkZGRkn5aze+uu+7SkSNHtH37drdyFi9erAULFigrK8t5PsvWrVv10EMPacyYMR6dB/TNN9/o8ccf17BhwzRo0CANHjxYGRkZHj2uRx55RDt27FB+fr4iIyM1c+ZMvffee3rmmWd09913KzY21u2szz77TMnJyerYsaPq16+vTz75RB06dNCXX36pJ598Ut27d3crZ9CgQVq2bNkZ248eParvv//e7UUwBg4cqH/+858qLCxUfHy83nnnHbVs2VI//vij83t39O/fXykpKWratKkOHjyoMWPGKCMjQydOnNBtt92mlStXupXzzjvvaM+ePfL19VXLli0VEhKiDh06KCMjw6PVFk2t2jh37lyVlpbq5ptvduYUFRVp8eLFzpkB7pg/f758fX2d9Rw4cECFhYXaunWrx6tIlpaW6h//+If279+v3bt3u/0cnS47O1v79+9Xt27dFBYWpq+//lqWZenzzz/3qKZly5Zp3759CggIUHl5uZo1a6Ybb7xROTk5bufk5OSorKzsvJ+zjz/+WJMnT5bD4dBjjz2mF154QVdeeaW++OIL3XfffW6P2Kanp+tf//qXrr32Wm3btk1//etf1a5dO40aNUpJSUkejbLv3LlThYWFOnTokPbs2aONGzdq+PDhHp8fZ2r10D179iggIEChoaHOnHbt2uno0aMaNWqURzkNGjRw1rNt2za1bNnSOT3YUyaO6z179qh+/foqKyvThg0b9M4776hz5866917PT/YeMmSIUlJSFBcXp2XLlungwYO688473X4t81Ytmnj/AkA/HP2ytktwctm49O/fX6+88sqv/rKnUyCAi8GpU6e0fft2HTp0SA6Hw/lBz5NpCCbl5eXJbrdXmhpRVFSkt99+26M3xX379qlly5bO73/+oHcuq8OY+qBXUlKiw4cP67e//a3zQ97vf/97j3OKioqUm5urvXv3qqKiwvlBLyQkxO2MnJwct5scV0x+0JsxY4auu+66c/6gN336dH355ZcKDw/XqlWrNGHCBOf0Hk8azunTp2vHjh264oortHr1ao0fP75O5EiVG7J9+/Zp5cqVevrppz1uyH65r8/3sZl4zp5//nl9+eWXxvbR/v371aNHDzVo0ECbN29WmzZttHnz5nNqWvv376+wsDCVlpbqxIkTeu+999zOmT9/vhYuXCiHw6EuXbooPz9fvXv31tq1a9WxY0e3p5zVRM51113n9vQsU/X87JdN2dKlS7Vt2zbdeOONHjVlpmu65ZZblJaWVumPRDfffLNzqu+FisbFQ66GYzyZJgHAsn744QeXXxd7zuk+/vhj65FHHvH490zW4+376PDhw9aqVavOmMbqjm+//dZatWqVtWfPHsuyfpomdvToUbd/PyYmxiorK7Ms66fpbzfddJOVlZVlWZZn7w8mc36eCuMN9Tz//PPWyJEjrb/97W9W165drWXLljl/5unUGm/c16b20ahRo857H5na1zExMVZJSYm1b98+69prr3VOgT116lSl6ZUXa45lWdbrr79u9ezZ0+rRo4c1ceJEa9SoUdaiRYusxMREa9asWbVSk2VZ1vjx463Fixdb/fv3t3bt2mU9/vjj1oMPPuhxjrcJ+c3VXv/lTVyeHdWxY8ea6p+AOsHECnd1Oef0pcsvv/xyJSUlObe7s3S5yXp+Lctms3k8Jba69pEk5zSz/fv3u72P9u/fL39//0q/+7Pi4mK3cixDqy2azPmZN9Rjchl9b9zX3rSPcnJytGzZMvn6+p5XjolVP+tyjmTmkg6ma5J+WiVz1qxZ8vPz01//+ld16dJFTz75pMc5uMDVdKcE1GWFhYXWzTffbG3evJmcs4iJibGuueYaq2fPntZNN91k3XTTTc7/79mzZ43XYzKrOvfRL/dVTebMnDnTio2NrXQy9ubNm60uXbpYHTt2dLuWuprTv3//SidDf/3119YNN9xgbdiwweMRF297bN62j0zl/POf/7RGjBhRKeurr76ybrnlFmvmzJkXfY5l/bQQgsPhsCzLsv71r385t586dcqjBRVM1lSX1fZoyoU24kLjAhhmYoW7uppjsuEw9bhMZnnTPjKVY2q1xbqYY+rDvcmavC3H2xopyzK36mddzTHZcMa1K7UAAAQGSURBVJhcYbV79+5W27Ztrc6dO1udO3d2/v+tt95qffXVV+eU6Q2a/+Yqr//yJi5PzgcA07Zv367U1FT9/e9/r+1SvJapfcS+rn7r169XcHCwrrjiCue2/Px8zZs3T5MmTarFyryHqX3Evq45mzZtqnSh42+//Vbff/+9kQVIztX48ePVs2dP5/Xn1q5dq+zsbA0fPlzPPPOM3nzzzVqr7XyEXHJ1bZdQpQPHvqrtEpxoXAAAAODVBg4cqMzMzErbhgwZovT09HNaVt9b0Lh4xqfqmwAAAAC1p3HjxkpNTdWpU6dUUlKi1NRUBQYGKi8vTw6Ho7bLQw1hxAUAAABeLf//2rt7lbjaKArAexDBwkwwMH6CxRQxELWzm06YTm0svAytLezEK1AbwXoqtbARRLDzFgRJJw5fYSAY0ELmpJsmEDn58d0Jz1NOtdrFXuedfj+2t7fj6uoqRkZGotPpxNbWVpydnUW73Y7FxcXSEX/Kf28/lo7wov+/XJeOMKS4AACQ2u7ubqyvr5eO8dspLvWYigEAkNr5+XnpCCTwwz+gBACA0iYmJmJpaSnm5+djbGxs+Pvf/mriIAyf6lBcAABIbWVlpXQEElBcAABIbW1tLR4eHuLx8TGqqorBYBC3t7elY/HKFBcAAFLb39+Pw8PDeH5+jmazGff39zE7OxvHx8elo/0Sb2TV4+N8AABSOzo6isvLy1heXo5erxd7e3vRarVKx+KVKS4AAKTWarWi2WzGzMxMXF9fR7fbjbu7u9KxeGWmYgAApDY+Ph6np6cxNzcXvV4vpqam4unpqXSsXzYwFavFxQUAgJROTk4iImJnZyf6/X50Op2YnJyMzc3N2NjYKJyO19aofBUEAEBCq6urw/LyL3r35kPpCC/6/HBTOsKQqRgAABTgflCP4gIAQEo3NzfR7Xa/+72qqmg0GnFxcVEgFaUoLgAApNRut+Pg4KB0DJJQXAAASGl0dDSmp6dLx/hjBmEqVodXxQAASGlhYaF0BBLxqhgAABTwdvx96Qgv+vL1U+kIQy4uAABAer5xAQCAAgyf6nFxAQAA0lNcAACA9EzFAACggIGpWC0uLgAAQHqKCwAAkJ6pGAAAFFCFqVgdLi4AAEB6igsAAJCeqRgAABTgVbF6XFwAAID0FBcAACA9UzEAACigMhWrxcUFAABIT3EBAADSU1wAAID0fOMCAAAFVOEblzpcXAAAgPQUFwAAID1TMQAAKMBzyPW4uAAAAOkpLgAAQHqmYgAAUICpWD0uLgAAQHqKCwAAkJ6pGAAAFGAoVo+LCwAAkJ7iAgAApNeoPGcAAAAk5+ICAACkp7gAAADpKS4AAEB6igsAAJCe4gIAAKSnuAAAAOl9A5dW0gZsa9+OAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sb\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "pdFtsData['Target'] = y_train_g\n",
    "C_mat = pdFtsData.corr()\n",
    "fig = plt.figure(figsize = (15,15))\n",
    "\n",
    "sb.heatmap(C_mat, vmax = 0.8, square = True)\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 构建模型"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "Model: \"sequential_3\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ndense_11 (Dense)             (None, 128)               4736      \n_________________________________________________________________\ndense_12 (Dense)             (None, 256)               33024     \n_________________________________________________________________\ndense_13 (Dense)             (None, 256)               65792     \n_________________________________________________________________\ndense_14 (Dense)             (None, 256)               65792     \n_________________________________________________________________\ndense_15 (Dense)             (None, 1)                 257       \n=================================================================\nTotal params: 169,601\nTrainable params: 169,601\nNon-trainable params: 0\n_________________________________________________________________\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Flatten\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "\n",
    "NN_model = Sequential()\n",
    "\n",
    "# The Input Layer :\n",
    "NN_model.add(Dense(128, kernel_initializer='normal',input_dim = X_train_g.shape[1], activation='relu'))\n",
    "\n",
    "# The Hidden Layers :\n",
    "NN_model.add(Dense(256, kernel_initializer='normal',activation='relu'))\n",
    "NN_model.add(Dense(256, kernel_initializer='normal',activation='relu'))\n",
    "NN_model.add(Dense(256, kernel_initializer='normal',activation='relu'))\n",
    "\n",
    "# The Output Layer :\n",
    "NN_model.add(Dense(1, kernel_initializer='normal',activation='linear'))\n",
    "\n",
    "# Compile the network :\n",
    "NN_model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mean_absolute_error'])\n",
    "NN_model.summary()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [],
   "source": [
    "checkpoint_name = 'Weights-{epoch:03d}--{val_loss:.5f}.hdf5'\n",
    "checkpoint = ModelCheckpoint(checkpoint_name, monitor='val_loss', verbose = 1, save_best_only = True, mode ='auto')\n",
    "callbacks_list = [checkpoint]\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "Train on 1417 samples, validate on 355 samples\nEpoch 1/500\n",
      "\r  32/1417 [..............................] - ETA: 5s - loss: 1.0694 - mean_absolute_error: 1.0694",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.4609 - mean_absolute_error: 0.4609",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 178us/step - loss: 0.3539 - mean_absolute_error: 0.3539 - val_loss: 0.2937 - val_mean_absolute_error: 0.2937\n",
      "\nEpoch 00001: val_loss improved from inf to 0.29373, saving model to Weights-001--0.29373.hdf5\n",
      "Epoch 2/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.1078 - mean_absolute_error: 0.1078",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.1183 - mean_absolute_error: 0.1183",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 54us/step - loss: 0.1125 - mean_absolute_error: 0.1125 - val_loss: 0.2259 - val_mean_absolute_error: 0.2259\n",
      "\nEpoch 00002: val_loss improved from 0.29373 to 0.22587, saving model to Weights-002--0.22587.hdf5\nEpoch 3/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0781 - mean_absolute_error: 0.0781",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0839 - mean_absolute_error: 0.0839",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0822 - mean_absolute_error: 0.0822 - val_loss: 0.1491 - val_mean_absolute_error: 0.1491\n",
      "\nEpoch 00003: val_loss improved from 0.22587 to 0.14908, saving model to Weights-003--0.14908.hdf5\nEpoch 4/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0929 - mean_absolute_error: 0.0929",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1248/1417 [=========================>....] - ETA: 0s - loss: 0.0831 - mean_absolute_error: 0.0831",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 49us/step - loss: 0.0803 - mean_absolute_error: 0.0803 - val_loss: 0.1367 - val_mean_absolute_error: 0.1367\n",
      "\nEpoch 00004: val_loss improved from 0.14908 to 0.13668, saving model to Weights-004--0.13668.hdf5\nEpoch 5/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0519 - mean_absolute_error: 0.0519",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 736/1417 [==============>...............] - ETA: 0s - loss: 0.0797 - mean_absolute_error: 0.0797",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 73us/step - loss: 0.0708 - mean_absolute_error: 0.0708 - val_loss: 0.2133 - val_mean_absolute_error: 0.2133\n",
      "\nEpoch 00005: val_loss did not improve from 0.13668\nEpoch 6/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.1085 - mean_absolute_error: 0.1085",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0796 - mean_absolute_error: 0.0796",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0741 - mean_absolute_error: 0.0741 - val_loss: 0.1220 - val_mean_absolute_error: 0.1220\n",
      "\nEpoch 00006: val_loss improved from 0.13668 to 0.12197, saving model to Weights-006--0.12197.hdf5\nEpoch 7/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0363 - mean_absolute_error: 0.0363",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0559 - mean_absolute_error: 0.0559",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 53us/step - loss: 0.0543 - mean_absolute_error: 0.0543 - val_loss: 0.1382 - val_mean_absolute_error: 0.1382\n",
      "\nEpoch 00007: val_loss did not improve from 0.12197\nEpoch 8/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0781 - mean_absolute_error: 0.0781",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0649 - mean_absolute_error: 0.0649",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0634 - mean_absolute_error: 0.0634 - val_loss: 0.1674 - val_mean_absolute_error: 0.1674\n",
      "\nEpoch 00008: val_loss did not improve from 0.12197\nEpoch 9/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0491 - mean_absolute_error: 0.0491",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 800/1417 [===============>..............] - ETA: 0s - loss: 0.0598 - mean_absolute_error: 0.0598",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0568 - mean_absolute_error: 0.0568 - val_loss: 0.2242 - val_mean_absolute_error: 0.2242\n",
      "\nEpoch 00009: val_loss did not improve from 0.12197\nEpoch 10/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0664 - mean_absolute_error: 0.0664",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0564 - mean_absolute_error: 0.0564",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 53us/step - loss: 0.0542 - mean_absolute_error: 0.0542 - val_loss: 0.1223 - val_mean_absolute_error: 0.1223\n",
      "\nEpoch 00010: val_loss did not improve from 0.12197\nEpoch 11/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0311 - mean_absolute_error: 0.0311",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0473 - mean_absolute_error: 0.0473",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0486 - mean_absolute_error: 0.0486 - val_loss: 0.1686 - val_mean_absolute_error: 0.1686\n",
      "\nEpoch 00011: val_loss did not improve from 0.12197\nEpoch 12/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0504 - mean_absolute_error: 0.0504",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0579 - mean_absolute_error: 0.0579",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 53us/step - loss: 0.0557 - mean_absolute_error: 0.0557 - val_loss: 0.1106 - val_mean_absolute_error: 0.1106\n",
      "\nEpoch 00012: val_loss improved from 0.12197 to 0.11057, saving model to Weights-012--0.11057.hdf5\nEpoch 13/500",
      "\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0371 - mean_absolute_error: 0.0371",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 736/1417 [==============>...............] - ETA: 0s - loss: 0.0466 - mean_absolute_error: 0.0466",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 72us/step - loss: 0.0472 - mean_absolute_error: 0.0472 - val_loss: 0.1359 - val_mean_absolute_error: 0.1359\n",
      "\nEpoch 00013: val_loss did not improve from 0.11057\nEpoch 14/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0586 - mean_absolute_error: 0.0586",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0541 - mean_absolute_error: 0.0541",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0519 - mean_absolute_error: 0.0519 - val_loss: 0.1051 - val_mean_absolute_error: 0.1051\n",
      "\nEpoch 00014: val_loss improved from 0.11057 to 0.10505, saving model to Weights-014--0.10505.hdf5\nEpoch 15/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0466 - mean_absolute_error: 0.0466",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0541 - mean_absolute_error: 0.0541",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0573 - mean_absolute_error: 0.0573 - val_loss: 0.1690 - val_mean_absolute_error: 0.1690\n",
      "\nEpoch 00015: val_loss did not improve from 0.10505\nEpoch 16/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0696 - mean_absolute_error: 0.0696",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 768/1417 [===============>..............] - ETA: 0s - loss: 0.0636 - mean_absolute_error: 0.0636",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1376/1417 [============================>.] - ETA: 0s - loss: 0.0558 - mean_absolute_error: 0.0558",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 85us/step - loss: 0.0554 - mean_absolute_error: 0.0554 - val_loss: 0.1222 - val_mean_absolute_error: 0.1222\n",
      "\nEpoch 00016: val_loss did not improve from 0.10505\nEpoch 17/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0308 - mean_absolute_error: 0.0308",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0512 - mean_absolute_error: 0.0512",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0521 - mean_absolute_error: 0.0521 - val_loss: 0.1600 - val_mean_absolute_error: 0.1600\n",
      "\nEpoch 00017: val_loss did not improve from 0.10505\nEpoch 18/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0500 - mean_absolute_error: 0.0500",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0449 - mean_absolute_error: 0.0449",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0447 - mean_absolute_error: 0.0447 - val_loss: 0.1396 - val_mean_absolute_error: 0.1396\n",
      "\nEpoch 00018: val_loss did not improve from 0.10505\nEpoch 19/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0510 - mean_absolute_error: 0.0510",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0720 - mean_absolute_error: 0.0720",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0732 - mean_absolute_error: 0.0732 - val_loss: 0.1298 - val_mean_absolute_error: 0.1298\n",
      "\nEpoch 00019: val_loss did not improve from 0.10505\nEpoch 20/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0861 - mean_absolute_error: 0.0861",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0464 - mean_absolute_error: 0.0464",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0493 - mean_absolute_error: 0.0493 - val_loss: 0.1536 - val_mean_absolute_error: 0.1536\n",
      "\nEpoch 00020: val_loss did not improve from 0.10505\nEpoch 21/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0407 - mean_absolute_error: 0.0407",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0601 - mean_absolute_error: 0.0601",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0619 - mean_absolute_error: 0.0619 - val_loss: 0.1601 - val_mean_absolute_error: 0.1601\n",
      "\nEpoch 00021: val_loss did not improve from 0.10505\nEpoch 22/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0724 - mean_absolute_error: 0.0724",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0509 - mean_absolute_error: 0.0509",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0475 - mean_absolute_error: 0.0475 - val_loss: 0.1001 - val_mean_absolute_error: 0.1001\n",
      "\nEpoch 00022: val_loss improved from 0.10505 to 0.10006, saving model to Weights-022--0.10006.hdf5\nEpoch 23/500",
      "\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0334 - mean_absolute_error: 0.0334",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0413 - mean_absolute_error: 0.0413",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0432 - mean_absolute_error: 0.0432 - val_loss: 0.2206 - val_mean_absolute_error: 0.2206\n",
      "\nEpoch 00023: val_loss did not improve from 0.10006\nEpoch 24/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0599 - mean_absolute_error: 0.0599",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 768/1417 [===============>..............] - ETA: 0s - loss: 0.0610 - mean_absolute_error: 0.0610",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0542 - mean_absolute_error: 0.0542 - val_loss: 0.1188 - val_mean_absolute_error: 0.1188\n",
      "\nEpoch 00024: val_loss did not improve from 0.10006\nEpoch 25/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0511 - mean_absolute_error: 0.0511",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0467 - mean_absolute_error: 0.0467",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0434 - mean_absolute_error: 0.0434 - val_loss: 0.1448 - val_mean_absolute_error: 0.1448\n",
      "\nEpoch 00025: val_loss did not improve from 0.10006\nEpoch 26/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0435 - mean_absolute_error: 0.0435",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 864/1417 [=================>............] - ETA: 0s - loss: 0.0507 - mean_absolute_error: 0.0507",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0486 - mean_absolute_error: 0.0486 - val_loss: 0.0975 - val_mean_absolute_error: 0.0975\n",
      "\nEpoch 00026: val_loss improved from 0.10006 to 0.09749, saving model to Weights-026--0.09749.hdf5\n",
      "Epoch 27/500",
      "\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0510 - mean_absolute_error: 0.0510",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0521 - mean_absolute_error: 0.0521",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0492 - mean_absolute_error: 0.0492 - val_loss: 0.1276 - val_mean_absolute_error: 0.1276\n",
      "\nEpoch 00027: val_loss did not improve from 0.09749\nEpoch 28/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0434 - mean_absolute_error: 0.0434",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0419 - mean_absolute_error: 0.0419",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0407 - mean_absolute_error: 0.0407 - val_loss: 0.1272 - val_mean_absolute_error: 0.1272\n",
      "\nEpoch 00028: val_loss did not improve from 0.09749\nEpoch 29/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0418 - mean_absolute_error: 0.0418",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0421 - mean_absolute_error: 0.0421",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0432 - mean_absolute_error: 0.0432 - val_loss: 0.1068 - val_mean_absolute_error: 0.1068\n",
      "\nEpoch 00029: val_loss did not improve from 0.09749\nEpoch 30/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0414 - mean_absolute_error: 0.0414",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0435 - mean_absolute_error: 0.0435",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0428 - mean_absolute_error: 0.0428 - val_loss: 0.1154 - val_mean_absolute_error: 0.1154\n",
      "\nEpoch 00030: val_loss did not improve from 0.09749\nEpoch 31/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0389 - mean_absolute_error: 0.0389",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0459 - mean_absolute_error: 0.0459",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 75us/step - loss: 0.0444 - mean_absolute_error: 0.0444 - val_loss: 0.0992 - val_mean_absolute_error: 0.0992\n",
      "\nEpoch 00031: val_loss did not improve from 0.09749\nEpoch 32/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0405 - mean_absolute_error: 0.0405",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0391 - mean_absolute_error: 0.0391",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 68us/step - loss: 0.0393 - mean_absolute_error: 0.0393 - val_loss: 0.1485 - val_mean_absolute_error: 0.1485\n",
      "\nEpoch 00032: val_loss did not improve from 0.09749\nEpoch 33/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0369 - mean_absolute_error: 0.0369",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0420 - mean_absolute_error: 0.0420",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0423 - mean_absolute_error: 0.0423 - val_loss: 0.1288 - val_mean_absolute_error: 0.1288\n",
      "\nEpoch 00033: val_loss did not improve from 0.09749\nEpoch 34/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0723 - mean_absolute_error: 0.0723",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0463 - mean_absolute_error: 0.0463",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0473 - mean_absolute_error: 0.0473 - val_loss: 0.1216 - val_mean_absolute_error: 0.1216\n",
      "\nEpoch 00034: val_loss did not improve from 0.09749\nEpoch 35/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0458 - mean_absolute_error: 0.0458",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 672/1417 [=============>................] - ETA: 0s - loss: 0.0421 - mean_absolute_error: 0.0421",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 74us/step - loss: 0.0430 - mean_absolute_error: 0.0430 - val_loss: 0.1116 - val_mean_absolute_error: 0.1116\n",
      "\nEpoch 00035: val_loss did not improve from 0.09749\nEpoch 36/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0266 - mean_absolute_error: 0.0266",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0486 - mean_absolute_error: 0.0486",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0484 - mean_absolute_error: 0.0484 - val_loss: 0.0999 - val_mean_absolute_error: 0.0999\n",
      "\nEpoch 00036: val_loss did not improve from 0.09749\nEpoch 37/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0741 - mean_absolute_error: 0.0741",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0637 - mean_absolute_error: 0.0637",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0555 - mean_absolute_error: 0.0555 - val_loss: 0.1018 - val_mean_absolute_error: 0.1018\n",
      "\nEpoch 00037: val_loss did not improve from 0.09749\nEpoch 38/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0413 - mean_absolute_error: 0.0413",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0414 - mean_absolute_error: 0.0414",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 65us/step - loss: 0.0416 - mean_absolute_error: 0.0416 - val_loss: 0.1085 - val_mean_absolute_error: 0.1085\n",
      "\nEpoch 00038: val_loss did not improve from 0.09749\nEpoch 39/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0399 - mean_absolute_error: 0.0399",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0498 - mean_absolute_error: 0.0498",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 65us/step - loss: 0.0473 - mean_absolute_error: 0.0473 - val_loss: 0.1016 - val_mean_absolute_error: 0.1016\n",
      "\nEpoch 00039: val_loss did not improve from 0.09749\nEpoch 40/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0413 - mean_absolute_error: 0.0413",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0390 - mean_absolute_error: 0.0390",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0386 - mean_absolute_error: 0.0386 - val_loss: 0.1136 - val_mean_absolute_error: 0.1136\n",
      "\nEpoch 00040: val_loss did not improve from 0.09749\nEpoch 41/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0485 - mean_absolute_error: 0.0485",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0475 - mean_absolute_error: 0.0475",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0490 - mean_absolute_error: 0.0490 - val_loss: 0.1046 - val_mean_absolute_error: 0.1046\n",
      "\nEpoch 00041: val_loss did not improve from 0.09749\nEpoch 42/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0328 - mean_absolute_error: 0.0328",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 800/1417 [===============>..............] - ETA: 0s - loss: 0.0450 - mean_absolute_error: 0.0450",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 77us/step - loss: 0.0440 - mean_absolute_error: 0.0440 - val_loss: 0.1873 - val_mean_absolute_error: 0.1873\n",
      "\nEpoch 00042: val_loss did not improve from 0.09749\nEpoch 43/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0547 - mean_absolute_error: 0.0547",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0439 - mean_absolute_error: 0.0439",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0424 - mean_absolute_error: 0.0424 - val_loss: 0.1017 - val_mean_absolute_error: 0.1017\n",
      "\nEpoch 00043: val_loss did not improve from 0.09749\nEpoch 44/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0434 - mean_absolute_error: 0.0434",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 800/1417 [===============>..............] - ETA: 0s - loss: 0.0572 - mean_absolute_error: 0.0572",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0519 - mean_absolute_error: 0.0519 - val_loss: 0.1158 - val_mean_absolute_error: 0.1158\n",
      "\nEpoch 00044: val_loss did not improve from 0.09749\nEpoch 45/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0377 - mean_absolute_error: 0.0377",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 768/1417 [===============>..............] - ETA: 0s - loss: 0.0378 - mean_absolute_error: 0.0378",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 82us/step - loss: 0.0389 - mean_absolute_error: 0.0389 - val_loss: 0.1012 - val_mean_absolute_error: 0.1012\n",
      "\nEpoch 00045: val_loss did not improve from 0.09749\nEpoch 46/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0605 - mean_absolute_error: 0.0605",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0435 - mean_absolute_error: 0.0435",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0467 - mean_absolute_error: 0.0467 - val_loss: 0.1006 - val_mean_absolute_error: 0.1006\n",
      "\nEpoch 00046: val_loss did not improve from 0.09749\nEpoch 47/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0518 - mean_absolute_error: 0.0518",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0566 - mean_absolute_error: 0.0566",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0557 - mean_absolute_error: 0.0557 - val_loss: 0.1183 - val_mean_absolute_error: 0.1183\n",
      "\nEpoch 00047: val_loss did not improve from 0.09749\nEpoch 48/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0524 - mean_absolute_error: 0.0524",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0462 - mean_absolute_error: 0.0462",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0471 - mean_absolute_error: 0.0471 - val_loss: 0.0971 - val_mean_absolute_error: 0.0971\n",
      "\nEpoch 00048: val_loss improved from 0.09749 to 0.09710, saving model to Weights-048--0.09710.hdf5\nEpoch 49/500",
      "\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0244 - mean_absolute_error: 0.0244",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 736/1417 [==============>...............] - ETA: 0s - loss: 0.0535 - mean_absolute_error: 0.0535",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 71us/step - loss: 0.0485 - mean_absolute_error: 0.0485 - val_loss: 0.1703 - val_mean_absolute_error: 0.1703\n",
      "\nEpoch 00049: val_loss did not improve from 0.09710\nEpoch 50/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0428 - mean_absolute_error: 0.0428",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0407 - mean_absolute_error: 0.0407",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0424 - mean_absolute_error: 0.0424 - val_loss: 0.1326 - val_mean_absolute_error: 0.1326\n",
      "\nEpoch 00050: val_loss did not improve from 0.09710\nEpoch 51/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0660 - mean_absolute_error: 0.0660",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0544 - mean_absolute_error: 0.0544",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0510 - mean_absolute_error: 0.0510 - val_loss: 0.1022 - val_mean_absolute_error: 0.1022\n",
      "\nEpoch 00051: val_loss did not improve from 0.09710\nEpoch 52/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0429 - mean_absolute_error: 0.0429",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0397 - mean_absolute_error: 0.0397",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0383 - mean_absolute_error: 0.0383 - val_loss: 0.1089 - val_mean_absolute_error: 0.1089\n",
      "\nEpoch 00052: val_loss did not improve from 0.09710\nEpoch 53/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0415 - mean_absolute_error: 0.0415",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 736/1417 [==============>...............] - ETA: 0s - loss: 0.0403 - mean_absolute_error: 0.0403",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 71us/step - loss: 0.0427 - mean_absolute_error: 0.0427 - val_loss: 0.1077 - val_mean_absolute_error: 0.1077\n",
      "\nEpoch 00053: val_loss did not improve from 0.09710\nEpoch 54/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0548 - mean_absolute_error: 0.0548",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 704/1417 [=============>................] - ETA: 0s - loss: 0.0442 - mean_absolute_error: 0.0442",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 71us/step - loss: 0.0396 - mean_absolute_error: 0.0396 - val_loss: 0.1037 - val_mean_absolute_error: 0.1037\n",
      "\nEpoch 00054: val_loss did not improve from 0.09710\nEpoch 55/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0456 - mean_absolute_error: 0.0456",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0388 - mean_absolute_error: 0.0388",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0396 - mean_absolute_error: 0.0396 - val_loss: 0.1053 - val_mean_absolute_error: 0.1053\n",
      "\nEpoch 00055: val_loss did not improve from 0.09710\nEpoch 56/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0693 - mean_absolute_error: 0.0693",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0519 - mean_absolute_error: 0.0519",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0486 - mean_absolute_error: 0.0486 - val_loss: 0.1137 - val_mean_absolute_error: 0.1137\n",
      "\nEpoch 00056: val_loss did not improve from 0.09710\nEpoch 57/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0305 - mean_absolute_error: 0.0305",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0374 - mean_absolute_error: 0.0374",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0387 - mean_absolute_error: 0.0387 - val_loss: 0.1482 - val_mean_absolute_error: 0.1482\n",
      "\nEpoch 00057: val_loss did not improve from 0.09710\nEpoch 58/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0487 - mean_absolute_error: 0.0487",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0333 - mean_absolute_error: 0.0333",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0341 - mean_absolute_error: 0.0341 - val_loss: 0.1452 - val_mean_absolute_error: 0.1452\n",
      "\nEpoch 00058: val_loss did not improve from 0.09710\nEpoch 59/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0451 - mean_absolute_error: 0.0451",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0403 - mean_absolute_error: 0.0403",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0403 - mean_absolute_error: 0.0403 - val_loss: 0.1167 - val_mean_absolute_error: 0.1167\n",
      "\nEpoch 00059: val_loss did not improve from 0.09710\nEpoch 60/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0402 - mean_absolute_error: 0.0402",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0356 - mean_absolute_error: 0.0356",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0355 - mean_absolute_error: 0.0355 - val_loss: 0.1051 - val_mean_absolute_error: 0.1051\n",
      "\nEpoch 00060: val_loss did not improve from 0.09710\nEpoch 61/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0428 - mean_absolute_error: 0.0428",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0364 - mean_absolute_error: 0.0364",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0380 - mean_absolute_error: 0.0380 - val_loss: 0.1130 - val_mean_absolute_error: 0.1130\n",
      "\nEpoch 00061: val_loss did not improve from 0.09710\nEpoch 62/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0326 - mean_absolute_error: 0.0326",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0351 - mean_absolute_error: 0.0351",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0354 - mean_absolute_error: 0.0354 - val_loss: 0.1386 - val_mean_absolute_error: 0.1386\n",
      "\nEpoch 00062: val_loss did not improve from 0.09710\nEpoch 63/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0307 - mean_absolute_error: 0.0307",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0401 - mean_absolute_error: 0.0401",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0379 - mean_absolute_error: 0.0379 - val_loss: 0.0983 - val_mean_absolute_error: 0.0983\n",
      "\nEpoch 00063: val_loss did not improve from 0.09710\nEpoch 64/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0250 - mean_absolute_error: 0.0250",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0366 - mean_absolute_error: 0.0366",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0358 - mean_absolute_error: 0.0358 - val_loss: 0.0954 - val_mean_absolute_error: 0.0954\n",
      "\nEpoch 00064: val_loss improved from 0.09710 to 0.09537, saving model to Weights-064--0.09537.hdf5\nEpoch 65/500",
      "\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0341 - mean_absolute_error: 0.0341",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0423 - mean_absolute_error: 0.0423",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 65us/step - loss: 0.0432 - mean_absolute_error: 0.0431 - val_loss: 0.2275 - val_mean_absolute_error: 0.2275\n",
      "\nEpoch 00065: val_loss did not improve from 0.09537\nEpoch 66/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0839 - mean_absolute_error: 0.0839",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0464 - mean_absolute_error: 0.0464",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0433 - mean_absolute_error: 0.0433 - val_loss: 0.1263 - val_mean_absolute_error: 0.1263\n",
      "\nEpoch 00066: val_loss did not improve from 0.09537\nEpoch 67/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0372 - mean_absolute_error: 0.0372",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0388 - mean_absolute_error: 0.0388",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0395 - mean_absolute_error: 0.0395 - val_loss: 0.1075 - val_mean_absolute_error: 0.1075\n",
      "\nEpoch 00067: val_loss did not improve from 0.09537\nEpoch 68/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0325 - mean_absolute_error: 0.0325",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0400 - mean_absolute_error: 0.0400",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0393 - mean_absolute_error: 0.0393 - val_loss: 0.1479 - val_mean_absolute_error: 0.1479\n",
      "\nEpoch 00068: val_loss did not improve from 0.09537\nEpoch 69/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0401 - mean_absolute_error: 0.0401",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0367 - mean_absolute_error: 0.0367",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 62us/step - loss: 0.0383 - mean_absolute_error: 0.0383 - val_loss: 0.0978 - val_mean_absolute_error: 0.0978\n",
      "\nEpoch 00069: val_loss did not improve from 0.09537\nEpoch 70/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0342 - mean_absolute_error: 0.0342",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0350 - mean_absolute_error: 0.0350",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0364 - mean_absolute_error: 0.0364 - val_loss: 0.1173 - val_mean_absolute_error: 0.1173\n",
      "\nEpoch 00070: val_loss did not improve from 0.09537\nEpoch 71/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0410 - mean_absolute_error: 0.0410",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 864/1417 [=================>............] - ETA: 0s - loss: 0.0421 - mean_absolute_error: 0.0421",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 69us/step - loss: 0.0423 - mean_absolute_error: 0.0423 - val_loss: 0.0977 - val_mean_absolute_error: 0.0977\n",
      "\nEpoch 00071: val_loss did not improve from 0.09537\nEpoch 72/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0374 - mean_absolute_error: 0.0374",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 864/1417 [=================>............] - ETA: 0s - loss: 0.0406 - mean_absolute_error: 0.0406",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0414 - mean_absolute_error: 0.0414 - val_loss: 0.1074 - val_mean_absolute_error: 0.1074\n",
      "\nEpoch 00072: val_loss did not improve from 0.09537\nEpoch 73/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0366 - mean_absolute_error: 0.0366",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0402 - mean_absolute_error: 0.0402",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0405 - mean_absolute_error: 0.0405 - val_loss: 0.1384 - val_mean_absolute_error: 0.1384\n",
      "\nEpoch 00073: val_loss did not improve from 0.09537\nEpoch 74/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0369 - mean_absolute_error: 0.0369",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0409 - mean_absolute_error: 0.0409",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0388 - mean_absolute_error: 0.0388 - val_loss: 0.0969 - val_mean_absolute_error: 0.0969\n",
      "\nEpoch 00074: val_loss did not improve from 0.09537\nEpoch 75/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0509 - mean_absolute_error: 0.0509",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0427 - mean_absolute_error: 0.0427",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0390 - mean_absolute_error: 0.0390 - val_loss: 0.0961 - val_mean_absolute_error: 0.0961\n",
      "\nEpoch 00075: val_loss did not improve from 0.09537\nEpoch 76/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0358 - mean_absolute_error: 0.0358",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0313 - mean_absolute_error: 0.0313",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0309 - mean_absolute_error: 0.0309 - val_loss: 0.1016 - val_mean_absolute_error: 0.1016\n",
      "\nEpoch 00076: val_loss did not improve from 0.09537\nEpoch 77/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0308 - mean_absolute_error: 0.0308",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0393 - mean_absolute_error: 0.0393",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0408 - mean_absolute_error: 0.0408 - val_loss: 0.1116 - val_mean_absolute_error: 0.1116\n",
      "\nEpoch 00077: val_loss did not improve from 0.09537\nEpoch 78/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0663 - mean_absolute_error: 0.0663",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0446 - mean_absolute_error: 0.0446",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0419 - mean_absolute_error: 0.0419 - val_loss: 0.1075 - val_mean_absolute_error: 0.1075\n",
      "\nEpoch 00078: val_loss did not improve from 0.09537\nEpoch 79/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0440 - mean_absolute_error: 0.0440",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0339 - mean_absolute_error: 0.0339",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0364 - mean_absolute_error: 0.0364 - val_loss: 0.0943 - val_mean_absolute_error: 0.0943\n",
      "\nEpoch 00079: val_loss improved from 0.09537 to 0.09431, saving model to Weights-079--0.09431.hdf5\nEpoch 80/500",
      "\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0744 - mean_absolute_error: 0.0744",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0416 - mean_absolute_error: 0.0416",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0407 - mean_absolute_error: 0.0407 - val_loss: 0.0936 - val_mean_absolute_error: 0.0936\n",
      "\nEpoch 00080: val_loss improved from 0.09431 to 0.09364, saving model to Weights-080--0.09364.hdf5\nEpoch 81/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0358 - mean_absolute_error: 0.0358",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0385 - mean_absolute_error: 0.0385",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 75us/step - loss: 0.0409 - mean_absolute_error: 0.0409 - val_loss: 0.0942 - val_mean_absolute_error: 0.0942\n",
      "\nEpoch 00081: val_loss did not improve from 0.09364\nEpoch 82/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0305 - mean_absolute_error: 0.0305",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0414 - mean_absolute_error: 0.0414",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0404 - mean_absolute_error: 0.0404 - val_loss: 0.0978 - val_mean_absolute_error: 0.0978\n",
      "\nEpoch 00082: val_loss did not improve from 0.09364\nEpoch 83/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0650 - mean_absolute_error: 0.0650",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0381 - mean_absolute_error: 0.0381",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0378 - mean_absolute_error: 0.0378 - val_loss: 0.1047 - val_mean_absolute_error: 0.1047\n",
      "\nEpoch 00083: val_loss did not improve from 0.09364\nEpoch 84/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0332 - mean_absolute_error: 0.0332",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0358 - mean_absolute_error: 0.0358",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0347 - mean_absolute_error: 0.0347 - val_loss: 0.0993 - val_mean_absolute_error: 0.0993\n",
      "\nEpoch 00084: val_loss did not improve from 0.09364\nEpoch 85/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0291 - mean_absolute_error: 0.0291",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0445 - mean_absolute_error: 0.0445",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 65us/step - loss: 0.0415 - mean_absolute_error: 0.0415 - val_loss: 0.0961 - val_mean_absolute_error: 0.0961\n",
      "\nEpoch 00085: val_loss did not improve from 0.09364\nEpoch 86/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0327 - mean_absolute_error: 0.0327",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0320 - mean_absolute_error: 0.0320",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0319 - mean_absolute_error: 0.0319 - val_loss: 0.1071 - val_mean_absolute_error: 0.1071\n",
      "\nEpoch 00086: val_loss did not improve from 0.09364\nEpoch 87/500",
      "\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0337 - mean_absolute_error: 0.0337",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0345 - mean_absolute_error: 0.0345",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0361 - mean_absolute_error: 0.0361 - val_loss: 0.1284 - val_mean_absolute_error: 0.1284\n",
      "\nEpoch 00087: val_loss did not improve from 0.09364\nEpoch 88/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0382 - mean_absolute_error: 0.0382",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0386 - mean_absolute_error: 0.0386",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0367 - mean_absolute_error: 0.0367 - val_loss: 0.0998 - val_mean_absolute_error: 0.0998\n",
      "\nEpoch 00088: val_loss did not improve from 0.09364\nEpoch 89/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0405 - mean_absolute_error: 0.0405",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 800/1417 [===============>..............] - ETA: 0s - loss: 0.0445 - mean_absolute_error: 0.0445",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 76us/step - loss: 0.0407 - mean_absolute_error: 0.0407 - val_loss: 0.1015 - val_mean_absolute_error: 0.1015\n",
      "\nEpoch 00089: val_loss did not improve from 0.09364\nEpoch 90/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0387 - mean_absolute_error: 0.0387",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0317 - mean_absolute_error: 0.0317",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0333 - mean_absolute_error: 0.0333 - val_loss: 0.0982 - val_mean_absolute_error: 0.0982\n",
      "\nEpoch 00090: val_loss did not improve from 0.09364\nEpoch 91/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0311 - mean_absolute_error: 0.0311",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0367 - mean_absolute_error: 0.0367",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0343 - mean_absolute_error: 0.0343 - val_loss: 0.1118 - val_mean_absolute_error: 0.1118\n",
      "\nEpoch 00091: val_loss did not improve from 0.09364\nEpoch 92/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0311 - mean_absolute_error: 0.0311",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0358 - mean_absolute_error: 0.0358",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0351 - mean_absolute_error: 0.0351 - val_loss: 0.1218 - val_mean_absolute_error: 0.1218\n",
      "\nEpoch 00092: val_loss did not improve from 0.09364\nEpoch 93/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0396 - mean_absolute_error: 0.0396",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 736/1417 [==============>...............] - ETA: 0s - loss: 0.0451 - mean_absolute_error: 0.0451",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 75us/step - loss: 0.0416 - mean_absolute_error: 0.0416 - val_loss: 0.1045 - val_mean_absolute_error: 0.1045\n",
      "\nEpoch 00093: val_loss did not improve from 0.09364\nEpoch 94/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0633 - mean_absolute_error: 0.0633",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0398 - mean_absolute_error: 0.0398",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0387 - mean_absolute_error: 0.0387 - val_loss: 0.1074 - val_mean_absolute_error: 0.1074\n",
      "\nEpoch 00094: val_loss did not improve from 0.09364\nEpoch 95/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0339 - mean_absolute_error: 0.0339",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0344 - mean_absolute_error: 0.0344",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0333 - mean_absolute_error: 0.0333 - val_loss: 0.0997 - val_mean_absolute_error: 0.0997\n",
      "\nEpoch 00095: val_loss did not improve from 0.09364\nEpoch 96/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0256 - mean_absolute_error: 0.0256",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0314 - mean_absolute_error: 0.0314",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================]",
      " - 0s 56us/step - loss: 0.0333 - mean_absolute_error: 0.0333 - val_loss: 0.1041 - val_mean_absolute_error: 0.1041\n",
      "\nEpoch 00096: val_loss did not improve from 0.09364\nEpoch 97/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0511 - mean_absolute_error: 0.0511",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0368 - mean_absolute_error: 0.0368",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0353 - mean_absolute_error: 0.0353 - val_loss: 0.1203 - val_mean_absolute_error: 0.1203\n",
      "\nEpoch 00097: val_loss did not improve from 0.09364\nEpoch 98/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0477 - mean_absolute_error: 0.0477",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0486 - mean_absolute_error: 0.0486",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0461 - mean_absolute_error: 0.0461 - val_loss: 0.1071 - val_mean_absolute_error: 0.1071\n",
      "\nEpoch 00098: val_loss did not improve from 0.09364\nEpoch 99/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0299 - mean_absolute_error: 0.0299",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0404 - mean_absolute_error: 0.0404",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0382 - mean_absolute_error: 0.0382 - val_loss: 0.0965 - val_mean_absolute_error: 0.0965\n",
      "\nEpoch 00099: val_loss did not improve from 0.09364\nEpoch 100/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0280 - mean_absolute_error: 0.0280",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 800/1417 [===============>..............] - ETA: 0s - loss: 0.0335 - mean_absolute_error: 0.0335",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 74us/step - loss: 0.0349 - mean_absolute_error: 0.0349 - val_loss: 0.1619 - val_mean_absolute_error: 0.1619\n",
      "\nEpoch 00100: val_loss did not improve from 0.09364\nEpoch 101/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0390 - mean_absolute_error: 0.0390",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 832/1417 [================>.............] - ETA: 0s - loss: 0.0367 - mean_absolute_error: 0.0367",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 72us/step - loss: 0.0381 - mean_absolute_error: 0.0381 - val_loss: 0.1375 - val_mean_absolute_error: 0.1375\n",
      "\nEpoch 00101: val_loss did not improve from 0.09364\nEpoch 102/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0314 - mean_absolute_error: 0.0314",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0412 - mean_absolute_error: 0.0412",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 67us/step - loss: 0.0370 - mean_absolute_error: 0.0370 - val_loss: 0.1007 - val_mean_absolute_error: 0.1007\n",
      "\nEpoch 00102: val_loss did not improve from 0.09364\nEpoch 103/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0333 - mean_absolute_error: 0.0333",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 800/1417 [===============>..............] - ETA: 0s - loss: 0.0355 - mean_absolute_error: 0.0355",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1312/1417 [==========================>...] - ETA: 0s - loss: 0.0371 - mean_absolute_error: 0.0371",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 104us/step - loss: 0.0369 - mean_absolute_error: 0.0369 - val_loss: 0.1085 - val_mean_absolute_error: 0.1085\n",
      "\nEpoch 00103: val_loss did not improve from 0.09364\nEpoch 104/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0300 - mean_absolute_error: 0.0300",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 832/1417 [================>.............] - ETA: 0s - loss: 0.0310 - mean_absolute_error: 0.0310",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 68us/step - loss: 0.0372 - mean_absolute_error: 0.0372 - val_loss: 0.1136 - val_mean_absolute_error: 0.1136\n",
      "\nEpoch 00104: val_loss did not improve from 0.09364\nEpoch 105/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0397 - mean_absolute_error: 0.0397",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0489 - mean_absolute_error: 0.0489",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0512 - mean_absolute_error: 0.0512 - val_loss: 0.1010 - val_mean_absolute_error: 0.1010\n",
      "\nEpoch 00105: val_loss did not improve from 0.09364\nEpoch 106/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0712 - mean_absolute_error: 0.0712",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0504 - mean_absolute_error: 0.0504",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0471 - mean_absolute_error: 0.0471 - val_loss: 0.1639 - val_mean_absolute_error: 0.1639\n",
      "\nEpoch 00106: val_loss did not improve from 0.09364\nEpoch 107/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0683 - mean_absolute_error: 0.0683",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 704/1417 [=============>................] - ETA: 0s - loss: 0.0379 - mean_absolute_error: 0.0379",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 73us/step - loss: 0.0385 - mean_absolute_error: 0.0385 - val_loss: 0.1015 - val_mean_absolute_error: 0.1015\n",
      "\nEpoch 00107: val_loss did not improve from 0.09364\nEpoch 108/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0400 - mean_absolute_error: 0.0400",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0338 - mean_absolute_error: 0.0338",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0339 - mean_absolute_error: 0.0339 - val_loss: 0.0978 - val_mean_absolute_error: 0.0978\n",
      "\nEpoch 00108: val_loss did not improve from 0.09364\nEpoch 109/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0543 - mean_absolute_error: 0.0543",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0357 - mean_absolute_error: 0.0357",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0374 - mean_absolute_error: 0.0374 - val_loss: 0.1012 - val_mean_absolute_error: 0.1012\n",
      "\nEpoch 00109: val_loss did not improve from 0.09364\nEpoch 110/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0280 - mean_absolute_error: 0.0280",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0316 - mean_absolute_error: 0.0316",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0331 - mean_absolute_error: 0.0331 - val_loss: 0.1914 - val_mean_absolute_error: 0.1914\n",
      "\nEpoch 00110: val_loss did not improve from 0.09364\nEpoch 111/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0437 - mean_absolute_error: 0.0437",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0392 - mean_absolute_error: 0.0392",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 69us/step - loss: 0.0372 - mean_absolute_error: 0.0372 - val_loss: 0.1061 - val_mean_absolute_error: 0.1061\n",
      "\nEpoch 00111: val_loss did not improve from 0.09364\nEpoch 112/500",
      "\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0294 - mean_absolute_error: 0.0294",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0325 - mean_absolute_error: 0.0325",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0329 - mean_absolute_error: 0.0329 - val_loss: 0.1501 - val_mean_absolute_error: 0.1501\n",
      "\nEpoch 00112: val_loss did not improve from 0.09364\nEpoch 113/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0577 - mean_absolute_error: 0.0577",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0476 - mean_absolute_error: 0.0476",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0446 - mean_absolute_error: 0.0446 - val_loss: 0.1091 - val_mean_absolute_error: 0.1091\n",
      "\nEpoch 00113: val_loss did not improve from 0.09364\nEpoch 114/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0389 - mean_absolute_error: 0.0389",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0372 - mean_absolute_error: 0.0372",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0358 - mean_absolute_error: 0.0358 - val_loss: 0.1219 - val_mean_absolute_error: 0.1219\n",
      "\nEpoch 00114: val_loss did not improve from 0.09364\nEpoch 115/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0335 - mean_absolute_error: 0.0335",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 768/1417 [===============>..............] - ETA: 0s - loss: 0.0308 - mean_absolute_error: 0.0308",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 71us/step - loss: 0.0316 - mean_absolute_error: 0.0316 - val_loss: 0.1007 - val_mean_absolute_error: 0.1007\n",
      "\nEpoch 00115: val_loss did not improve from 0.09364\nEpoch 116/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0259 - mean_absolute_error: 0.0259",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0284 - mean_absolute_error: 0.0284",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0298 - mean_absolute_error: 0.0298 - val_loss: 0.1030 - val_mean_absolute_error: 0.1030\n",
      "\nEpoch 00116: val_loss did not improve from 0.09364\nEpoch 117/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0373 - mean_absolute_error: 0.0373",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0348 - mean_absolute_error: 0.0348",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0337 - mean_absolute_error: 0.0337 - val_loss: 0.0963 - val_mean_absolute_error: 0.0963\n",
      "\nEpoch 00117: val_loss did not improve from 0.09364\nEpoch 118/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0233 - mean_absolute_error: 0.0233",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0289 - mean_absolute_error: 0.0289",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0304 - mean_absolute_error: 0.0304 - val_loss: 0.0971 - val_mean_absolute_error: 0.0971\n",
      "\nEpoch 00118: val_loss did not improve from 0.09364\n",
      "Epoch 119/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0222 - mean_absolute_error: 0.0222",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0406 - mean_absolute_error: 0.0406",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0406 - mean_absolute_error: 0.0406 - val_loss: 0.1027 - val_mean_absolute_error: 0.1027\n",
      "\nEpoch 00119: val_loss did not improve from 0.09364\nEpoch 120/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0227 - mean_absolute_error: 0.0227",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0344 - mean_absolute_error: 0.0344",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0353 - mean_absolute_error: 0.0353 - val_loss: 0.1081 - val_mean_absolute_error: 0.1081\n",
      "\nEpoch 00120: val_loss did not improve from 0.09364\nEpoch 121/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0262 - mean_absolute_error: 0.0262",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0293 - mean_absolute_error: 0.0293",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0303 - mean_absolute_error: 0.0303 - val_loss: 0.1031 - val_mean_absolute_error: 0.1031\n",
      "\nEpoch 00121: val_loss did not improve from 0.09364\nEpoch 122/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0339 - mean_absolute_error: 0.0339",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0335 - mean_absolute_error: 0.0335",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0351 - mean_absolute_error: 0.0351 - val_loss: 0.1345 - val_mean_absolute_error: 0.1345\n",
      "\nEpoch 00122: val_loss did not improve from 0.09364\n",
      "Epoch 123/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0568 - mean_absolute_error: 0.0568",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 800/1417 [===============>..............] - ETA: 0s - loss: 0.0452 - mean_absolute_error: 0.0452",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 67us/step - loss: 0.0393 - mean_absolute_error: 0.0393 - val_loss: 0.1036 - val_mean_absolute_error: 0.1036\n",
      "\nEpoch 00123: val_loss did not improve from 0.09364\nEpoch 124/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0195 - mean_absolute_error: 0.0195",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0277 - mean_absolute_error: 0.0277",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0313 - mean_absolute_error: 0.0313 - val_loss: 0.1185 - val_mean_absolute_error: 0.1185\n",
      "\nEpoch 00124: val_loss did not improve from 0.09364\nEpoch 125/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0414 - mean_absolute_error: 0.0414",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 832/1417 [================>.............] - ETA: 0s - loss: 0.0394 - mean_absolute_error: 0.0394",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 83us/step - loss: 0.0352 - mean_absolute_error: 0.0352 - val_loss: 0.1373 - val_mean_absolute_error: 0.1373\n",
      "\nEpoch 00125: val_loss did not improve from 0.09364\nEpoch 126/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0358 - mean_absolute_error: 0.0358",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 832/1417 [================>.............] - ETA: 0s - loss: 0.0369 - mean_absolute_error: 0.0369",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 67us/step - loss: 0.0373 - mean_absolute_error: 0.0373 - val_loss: 0.1011 - val_mean_absolute_error: 0.1011\n",
      "\nEpoch 00126: val_loss did not improve from 0.09364\nEpoch 127/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0495 - mean_absolute_error: 0.0495",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0308 - mean_absolute_error: 0.0308",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0335 - mean_absolute_error: 0.0335 - val_loss: 0.1081 - val_mean_absolute_error: 0.1081\n",
      "\nEpoch 00127: val_loss did not improve from 0.09364\nEpoch 128/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0661 - mean_absolute_error: 0.0661",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0434 - mean_absolute_error: 0.0434",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0410 - mean_absolute_error: 0.0410 - val_loss: 0.1246 - val_mean_absolute_error: 0.1246\n",
      "\nEpoch 00128: val_loss did not improve from 0.09364\n",
      "Epoch 129/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0475 - mean_absolute_error: 0.0475",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0409 - mean_absolute_error: 0.0409",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 69us/step - loss: 0.0368 - mean_absolute_error: 0.0368 - val_loss: 0.1053 - val_mean_absolute_error: 0.1053\n",
      "\nEpoch 00129: val_loss did not improve from 0.09364\nEpoch 130/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0273 - mean_absolute_error: 0.0273",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0327 - mean_absolute_error: 0.0327",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0355 - mean_absolute_error: 0.0355 - val_loss: 0.0975 - val_mean_absolute_error: 0.0975\n",
      "\nEpoch 00130: val_loss did not improve from 0.09364\nEpoch 131/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0277 - mean_absolute_error: 0.0277",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 832/1417 [================>.............] - ETA: 0s - loss: 0.0320 - mean_absolute_error: 0.0320",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 67us/step - loss: 0.0327 - mean_absolute_error: 0.0327 - val_loss: 0.1308 - val_mean_absolute_error: 0.1308\n",
      "\nEpoch 00131: val_loss did not improve from 0.09364\nEpoch 132/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0328 - mean_absolute_error: 0.0328",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0304 - mean_absolute_error: 0.0304",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 72us/step - loss: 0.0308 - mean_absolute_error: 0.0308 - val_loss: 0.1226 - val_mean_absolute_error: 0.1226\n",
      "\nEpoch 00132: val_loss did not improve from 0.09364\nEpoch 133/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0348 - mean_absolute_error: 0.0348",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0323 - mean_absolute_error: 0.0323",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0318 - mean_absolute_error: 0.0318 - val_loss: 0.1004 - val_mean_absolute_error: 0.1004\n",
      "\nEpoch 00133: val_loss did not improve from 0.09364\nEpoch 134/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0349 - mean_absolute_error: 0.0349",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0309 - mean_absolute_error: 0.0309",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0313 - mean_absolute_error: 0.0313 - val_loss: 0.1056 - val_mean_absolute_error: 0.1056\n",
      "\nEpoch 00134: val_loss did not improve from 0.09364\nEpoch 135/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0280 - mean_absolute_error: 0.0280",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0355 - mean_absolute_error: 0.0355",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0340 - mean_absolute_error: 0.0340 - val_loss: 0.1355 - val_mean_absolute_error: 0.1355\n",
      "\nEpoch 00135: val_loss did not improve from 0.09364\nEpoch 136/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0299 - mean_absolute_error: 0.0299",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 832/1417 [================>.............] - ETA: 0s - loss: 0.0336 - mean_absolute_error: 0.0336",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0361 - mean_absolute_error: 0.0361 - val_loss: 0.1029 - val_mean_absolute_error: 0.1029\n",
      "\nEpoch 00136: val_loss did not improve from 0.09364\nEpoch 137/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0247 - mean_absolute_error: 0.0247",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0307 - mean_absolute_error: 0.0307",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0304 - mean_absolute_error: 0.0304 - val_loss: 0.0973 - val_mean_absolute_error: 0.0973\n",
      "\nEpoch 00137: val_loss did not improve from 0.09364\nEpoch 138/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0225 - mean_absolute_error: 0.0225",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0359 - mean_absolute_error: 0.0359",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0356 - mean_absolute_error: 0.0356 - val_loss: 0.1103 - val_mean_absolute_error: 0.1103\n",
      "\nEpoch 00138: val_loss did not improve from 0.09364\nEpoch 139/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0295 - mean_absolute_error: 0.0295",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0307 - mean_absolute_error: 0.0307",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0329 - mean_absolute_error: 0.0329 - val_loss: 0.1181 - val_mean_absolute_error: 0.1181\n",
      "\nEpoch 00139: val_loss did not improve from 0.09364\nEpoch 140/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0444 - mean_absolute_error: 0.0444",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0316 - mean_absolute_error: 0.0316",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0347 - mean_absolute_error: 0.0347 - val_loss: 0.0967 - val_mean_absolute_error: 0.0967\n",
      "\nEpoch 00140: val_loss did not improve from 0.09364\nEpoch 141/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0472 - mean_absolute_error: 0.0472",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0390 - mean_absolute_error: 0.0390",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0391 - mean_absolute_error: 0.0391 - val_loss: 0.0986 - val_mean_absolute_error: 0.0986\n",
      "\nEpoch 00141: val_loss did not improve from 0.09364\nEpoch 142/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0200 - mean_absolute_error: 0.0200",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 864/1417 [=================>............] - ETA: 0s - loss: 0.0357 - mean_absolute_error: 0.0357",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 65us/step - loss: 0.0371 - mean_absolute_error: 0.0371 - val_loss: 0.1086 - val_mean_absolute_error: 0.1086\n",
      "\nEpoch 00142: val_loss did not improve from 0.09364\nEpoch 143/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0285 - mean_absolute_error: 0.0285",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0306 - mean_absolute_error: 0.0306",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 62us/step - loss: 0.0318 - mean_absolute_error: 0.0318 - val_loss: 0.1001 - val_mean_absolute_error: 0.1001\n",
      "\nEpoch 00143: val_loss did not improve from 0.09364\nEpoch 144/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0365 - mean_absolute_error: 0.0365",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0370 - mean_absolute_error: 0.0370",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 79us/step - loss: 0.0378 - mean_absolute_error: 0.0378 - val_loss: 0.0966 - val_mean_absolute_error: 0.0966\n",
      "\nEpoch 00144: val_loss did not improve from 0.09364\nEpoch 145/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0450 - mean_absolute_error: 0.0450",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0382 - mean_absolute_error: 0.0382",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0379 - mean_absolute_error: 0.0379 - val_loss: 0.0968 - val_mean_absolute_error: 0.0968\n",
      "\nEpoch 00145: val_loss did not improve from 0.09364\nEpoch 146/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0553 - mean_absolute_error: 0.0553",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0360 - mean_absolute_error: 0.0360",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0365 - mean_absolute_error: 0.0365 - val_loss: 0.1415 - val_mean_absolute_error: 0.1415\n",
      "\nEpoch 00146: val_loss did not improve from 0.09364\nEpoch 147/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0396 - mean_absolute_error: 0.0396",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0395 - mean_absolute_error: 0.0395",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 62us/step - loss: 0.0375 - mean_absolute_error: 0.0375 - val_loss: 0.1127 - val_mean_absolute_error: 0.1127\n",
      "\nEpoch 00147: val_loss did not improve from 0.09364\nEpoch 148/500\n\r",
      "  32/1417 [..............................] - ETA: 0s - loss: 0.0246 - mean_absolute_error: 0.0246",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0391 - mean_absolute_error: 0.0391",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0397 - mean_absolute_error: 0.0397 - val_loss: 0.0965 - val_mean_absolute_error: 0.0965\n",
      "\nEpoch 00148: val_loss did not improve from 0.09364\nEpoch 149/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0318 - mean_absolute_error: 0.0318",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0311 - mean_absolute_error: 0.0311",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 65us/step - loss: 0.0328 - mean_absolute_error: 0.0328 - val_loss: 0.1060 - val_mean_absolute_error: 0.1060\n",
      "\nEpoch 00149: val_loss did not improve from 0.09364\nEpoch 150/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0328 - mean_absolute_error: 0.0328",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0333 - mean_absolute_error: 0.0333",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 62us/step - loss: 0.0316 - mean_absolute_error: 0.0316 - val_loss: 0.1404 - val_mean_absolute_error: 0.1404\n",
      "\nEpoch 00150: val_loss did not improve from 0.09364\nEpoch 151/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0331 - mean_absolute_error: 0.0331",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0342 - mean_absolute_error: 0.0342",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0330 - mean_absolute_error: 0.0330 - val_loss: 0.1079 - val_mean_absolute_error: 0.1079\n",
      "\nEpoch 00151: val_loss did not improve from 0.09364\nEpoch 152/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0350 - mean_absolute_error: 0.0350",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0306 - mean_absolute_error: 0.0306",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0321 - mean_absolute_error: 0.0321 - val_loss: 0.1600 - val_mean_absolute_error: 0.1600\n",
      "\nEpoch 00152: val_loss did not improve from 0.09364\nEpoch 153/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0292 - mean_absolute_error: 0.0292",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0340 - mean_absolute_error: 0.0340",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0351 - mean_absolute_error: 0.0351 - val_loss: 0.0990 - val_mean_absolute_error: 0.0990\n",
      "\nEpoch 00153: val_loss did not improve from 0.09364\nEpoch 154/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0358 - mean_absolute_error: 0.0358",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0308 - mean_absolute_error: 0.0308",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0317 - mean_absolute_error: 0.0317 - val_loss: 0.0956 - val_mean_absolute_error: 0.0956\n",
      "\nEpoch 00154: val_loss did not improve from 0.09364\nEpoch 155/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0394 - mean_absolute_error: 0.0394",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0361 - mean_absolute_error: 0.0361",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0353 - mean_absolute_error: 0.0353 - val_loss: 0.0952 - val_mean_absolute_error: 0.0952\n",
      "\nEpoch 00155: val_loss did not improve from 0.09364\nEpoch 156/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0248 - mean_absolute_error: 0.0248",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0295 - mean_absolute_error: 0.0295",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0283 - mean_absolute_error: 0.0283 - val_loss: 0.1119 - val_mean_absolute_error: 0.1119\n",
      "\nEpoch 00156: val_loss did not improve from 0.09364\nEpoch 157/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0193 - mean_absolute_error: 0.0193",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0322 - mean_absolute_error: 0.0322",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0318 - mean_absolute_error: 0.0318 - val_loss: 0.0989 - val_mean_absolute_error: 0.0989\n",
      "\nEpoch 00157: val_loss did not improve from 0.09364\nEpoch 158/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0303 - mean_absolute_error: 0.0303",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0359 - mean_absolute_error: 0.0359",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0359 - mean_absolute_error: 0.0359 - val_loss: 0.0946 - val_mean_absolute_error: 0.0946\n",
      "\nEpoch 00158: val_loss did not improve from 0.09364\nEpoch 159/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0288 - mean_absolute_error: 0.0288",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 672/1417 [=============>................] - ETA: 0s - loss: 0.0311 - mean_absolute_error: 0.0311",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 78us/step - loss: 0.0329 - mean_absolute_error: 0.0329 - val_loss: 0.0974 - val_mean_absolute_error: 0.0974\n",
      "\nEpoch 00159: val_loss did not improve from 0.09364\nEpoch 160/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0522 - mean_absolute_error: 0.0522",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0390 - mean_absolute_error: 0.0390",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0383 - mean_absolute_error: 0.0383 - val_loss: 0.1010 - val_mean_absolute_error: 0.1010\n",
      "\nEpoch 00160: val_loss did not improve from 0.09364\nEpoch 161/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0353 - mean_absolute_error: 0.0353",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0386 - mean_absolute_error: 0.0386",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0388 - mean_absolute_error: 0.0388 - val_loss: 0.0942 - val_mean_absolute_error: 0.0942\n",
      "\nEpoch 00161: val_loss did not improve from 0.09364\nEpoch 162/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0164 - mean_absolute_error: 0.0164",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0326 - mean_absolute_error: 0.0326",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0320 - mean_absolute_error: 0.0320 - val_loss: 0.1435 - val_mean_absolute_error: 0.1435\n",
      "\nEpoch 00162: val_loss did not improve from 0.09364\nEpoch 163/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0373 - mean_absolute_error: 0.0373",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0300 - mean_absolute_error: 0.0300",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0296 - mean_absolute_error: 0.0296 - val_loss: 0.1346 - val_mean_absolute_error: 0.1346\n",
      "\nEpoch 00163: val_loss did not improve from 0.09364\nEpoch 164/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0476 - mean_absolute_error: 0.0476",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0448 - mean_absolute_error: 0.0448",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0421 - mean_absolute_error: 0.0421 - val_loss: 0.1112 - val_mean_absolute_error: 0.1112\n",
      "\nEpoch 00164: val_loss did not improve from 0.09364\nEpoch 165/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0612 - mean_absolute_error: 0.0612",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0390 - mean_absolute_error: 0.0390",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 65us/step - loss: 0.0350 - mean_absolute_error: 0.0350 - val_loss: 0.1051 - val_mean_absolute_error: 0.1051\n",
      "\nEpoch 00165: val_loss did not improve from 0.09364\nEpoch 166/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0244 - mean_absolute_error: 0.0244",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0294 - mean_absolute_error: 0.0294",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0322 - mean_absolute_error: 0.0322 - val_loss: 0.1068 - val_mean_absolute_error: 0.1068\n",
      "\nEpoch 00166: val_loss did not improve from 0.09364\nEpoch 167/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0261 - mean_absolute_error: 0.0261",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0334 - mean_absolute_error: 0.0334",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0327 - mean_absolute_error: 0.0327 - val_loss: 0.0981 - val_mean_absolute_error: 0.0981\n",
      "\nEpoch 00167: val_loss did not improve from 0.09364\nEpoch 168/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0208 - mean_absolute_error: 0.0208",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0305 - mean_absolute_error: 0.0305",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0305 - mean_absolute_error: 0.0305 - val_loss: 0.1142 - val_mean_absolute_error: 0.1142\n",
      "\nEpoch 00168: val_loss did not improve from 0.09364\nEpoch 169/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0434 - mean_absolute_error: 0.0434",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0284 - mean_absolute_error: 0.0284",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0274 - mean_absolute_error: 0.0274 - val_loss: 0.1046 - val_mean_absolute_error: 0.1046\n",
      "\nEpoch 00169: val_loss did not improve from 0.09364\nEpoch 170/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0202 - mean_absolute_error: 0.0202",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 864/1417 [=================>............] - ETA: 0s - loss: 0.0279 - mean_absolute_error: 0.0279",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 70us/step - loss: 0.0294 - mean_absolute_error: 0.0294 - val_loss: 0.1452 - val_mean_absolute_error: 0.1452\n",
      "\nEpoch 00170: val_loss did not improve from 0.09364\nEpoch 171/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0280 - mean_absolute_error: 0.0280",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0349 - mean_absolute_error: 0.0349",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0342 - mean_absolute_error: 0.0342 - val_loss: 0.1063 - val_mean_absolute_error: 0.1063\n",
      "\nEpoch 00171: val_loss did not improve from 0.09364\nEpoch 172/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0180 - mean_absolute_error: 0.0180",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0269 - mean_absolute_error: 0.0269",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 62us/step - loss: 0.0278 - mean_absolute_error: 0.0278 - val_loss: 0.1380 - val_mean_absolute_error: 0.1380\n",
      "\nEpoch 00172: val_loss did not improve from 0.09364\nEpoch 173/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0333 - mean_absolute_error: 0.0333",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0302 - mean_absolute_error: 0.0302",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0311 - mean_absolute_error: 0.0311 - val_loss: 0.1059 - val_mean_absolute_error: 0.1059\n",
      "\nEpoch 00173: val_loss did not improve from 0.09364\nEpoch 174/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0397 - mean_absolute_error: 0.0397",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0315 - mean_absolute_error: 0.0315",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0322 - mean_absolute_error: 0.0322 - val_loss: 0.0976 - val_mean_absolute_error: 0.0976\n",
      "\nEpoch 00174: val_loss did not improve from 0.09364\nEpoch 175/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0547 - mean_absolute_error: 0.0547",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0308 - mean_absolute_error: 0.0308",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0330 - mean_absolute_error: 0.0330 - val_loss: 0.1200 - val_mean_absolute_error: 0.1200\n",
      "\nEpoch 00175: val_loss did not improve from 0.09364\nEpoch 176/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0583 - mean_absolute_error: 0.0583",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0321 - mean_absolute_error: 0.0321",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 62us/step - loss: 0.0309 - mean_absolute_error: 0.0309 - val_loss: 0.0997 - val_mean_absolute_error: 0.0997\n",
      "\nEpoch 00176: val_loss did not improve from 0.09364\n",
      "Epoch 177/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0217 - mean_absolute_error: 0.0217",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0293 - mean_absolute_error: 0.0293",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0302 - mean_absolute_error: 0.0302 - val_loss: 0.1176 - val_mean_absolute_error: 0.1176\n",
      "\nEpoch 00177: val_loss did not improve from 0.09364\nEpoch 178/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0199 - mean_absolute_error: 0.0199",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0273 - mean_absolute_error: 0.0273",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0304 - mean_absolute_error: 0.0304 - val_loss: 0.0991 - val_mean_absolute_error: 0.0991\n",
      "\nEpoch 00178: val_loss did not improve from 0.09364\nEpoch 179/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0330 - mean_absolute_error: 0.0330",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0341 - mean_absolute_error: 0.0341",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0327 - mean_absolute_error: 0.0327 - val_loss: 0.0997 - val_mean_absolute_error: 0.0997\n",
      "\nEpoch 00179: val_loss did not improve from 0.09364\nEpoch 180/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0244 - mean_absolute_error: 0.0244",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 864/1417 [=================>............] - ETA: 0s - loss: 0.0296 - mean_absolute_error: 0.0296",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 70us/step - loss: 0.0286 - mean_absolute_error: 0.0286 - val_loss: 0.1012 - val_mean_absolute_error: 0.1012\n",
      "\nEpoch 00180: val_loss did not improve from 0.09364\nEpoch 181/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0335 - mean_absolute_error: 0.0335",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0274 - mean_absolute_error: 0.0274",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 71us/step - loss: 0.0281 - mean_absolute_error: 0.0281 - val_loss: 0.1228 - val_mean_absolute_error: 0.1228\n",
      "\nEpoch 00181: val_loss did not improve from 0.09364\nEpoch 182/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0230 - mean_absolute_error: 0.0230",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 800/1417 [===============>..............] - ETA: 0s - loss: 0.0336 - mean_absolute_error: 0.0336",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 71us/step - loss: 0.0301 - mean_absolute_error: 0.0301 - val_loss: 0.1034 - val_mean_absolute_error: 0.1034\n",
      "\nEpoch 00182: val_loss did not improve from 0.09364\nEpoch 183/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0235 - mean_absolute_error: 0.0235",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0281 - mean_absolute_error: 0.0281",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 65us/step - loss: 0.0300 - mean_absolute_error: 0.0300 - val_loss: 0.1004 - val_mean_absolute_error: 0.1004\n",
      "\nEpoch 00183: val_loss did not improve from 0.09364\nEpoch 184/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0340 - mean_absolute_error: 0.0340",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0279 - mean_absolute_error: 0.0279",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 68us/step - loss: 0.0267 - mean_absolute_error: 0.0267 - val_loss: 0.0961 - val_mean_absolute_error: 0.0961\n",
      "\nEpoch 00184: val_loss did not improve from 0.09364\nEpoch 185/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0262 - mean_absolute_error: 0.0262",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 800/1417 [===============>..............] - ETA: 0s - loss: 0.0269 - mean_absolute_error: 0.0269",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 87us/step - loss: 0.0296 - mean_absolute_error: 0.0296 - val_loss: 0.1049 - val_mean_absolute_error: 0.1049\n",
      "\nEpoch 00185: val_loss did not improve from 0.09364\nEpoch 186/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0272 - mean_absolute_error: 0.0272",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 864/1417 [=================>............] - ETA: 0s - loss: 0.0296 - mean_absolute_error: 0.0296",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 67us/step - loss: 0.0287 - mean_absolute_error: 0.0287 - val_loss: 0.1098 - val_mean_absolute_error: 0.1098\n",
      "\nEpoch 00186: val_loss did not improve from 0.09364\nEpoch 187/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0501 - mean_absolute_error: 0.0501",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0329 - mean_absolute_error: 0.0329",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0341 - mean_absolute_error: 0.0341 - val_loss: 0.1108 - val_mean_absolute_error: 0.1108\n",
      "\nEpoch 00187: val_loss did not improve from 0.09364\nEpoch 188/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0382 - mean_absolute_error: 0.0382",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0282 - mean_absolute_error: 0.0282",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0283 - mean_absolute_error: 0.0283 - val_loss: 0.0983 - val_mean_absolute_error: 0.0983\n",
      "\nEpoch 00188: val_loss did not improve from 0.09364\n",
      "Epoch 189/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0306 - mean_absolute_error: 0.0306",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0302 - mean_absolute_error: 0.0302",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0304 - mean_absolute_error: 0.0304 - val_loss: 0.1021 - val_mean_absolute_error: 0.1021\n",
      "\nEpoch 00189: val_loss did not improve from 0.09364\nEpoch 190/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0291 - mean_absolute_error: 0.0291",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0384 - mean_absolute_error: 0.0384",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0382 - mean_absolute_error: 0.0382 - val_loss: 0.1296 - val_mean_absolute_error: 0.1296\n",
      "\nEpoch 00190: val_loss did not improve from 0.09364\n",
      "Epoch 191/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0501 - mean_absolute_error: 0.0501",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0337 - mean_absolute_error: 0.0337",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0339 - mean_absolute_error: 0.0339 - val_loss: 0.1046 - val_mean_absolute_error: 0.1046\n",
      "\nEpoch 00191: val_loss did not improve from 0.09364\nEpoch 192/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0251 - mean_absolute_error: 0.0251",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0290 - mean_absolute_error: 0.0290",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0302 - mean_absolute_error: 0.0302 - val_loss: 0.1410 - val_mean_absolute_error: 0.1410\n",
      "\nEpoch 00192: val_loss did not improve from 0.09364\nEpoch 193/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0342 - mean_absolute_error: 0.0342",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0309 - mean_absolute_error: 0.0309",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0307 - mean_absolute_error: 0.0307 - val_loss: 0.1108 - val_mean_absolute_error: 0.1108\n",
      "\nEpoch 00193: val_loss did not improve from 0.09364\nEpoch 194/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0207 - mean_absolute_error: 0.0207",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0258 - mean_absolute_error: 0.0258",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0258 - mean_absolute_error: 0.0258 - val_loss: 0.1038 - val_mean_absolute_error: 0.1038\n",
      "\nEpoch 00194: val_loss did not improve from 0.09364\nEpoch 195/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0249 - mean_absolute_error: 0.0249",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0272 - mean_absolute_error: 0.0272",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0262 - mean_absolute_error: 0.0262 - val_loss: 0.0974 - val_mean_absolute_error: 0.0974\n",
      "\nEpoch 00195: val_loss did not improve from 0.09364\nEpoch 196/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0357 - mean_absolute_error: 0.0357",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0260 - mean_absolute_error: 0.0260",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0259 - mean_absolute_error: 0.0259 - val_loss: 0.1033 - val_mean_absolute_error: 0.1033\n",
      "\nEpoch 00196: val_loss did not improve from 0.09364\nEpoch 197/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0284 - mean_absolute_error: 0.0284",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 832/1417 [================>.............] - ETA: 0s - loss: 0.0298 - mean_absolute_error: 0.0298",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0289 - mean_absolute_error: 0.0289 - val_loss: 0.1121 - val_mean_absolute_error: 0.1121\n",
      "\nEpoch 00197: val_loss did not improve from 0.09364\nEpoch 198/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0351 - mean_absolute_error: 0.0351",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0299 - mean_absolute_error: 0.0299",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0293 - mean_absolute_error: 0.0293 - val_loss: 0.0994 - val_mean_absolute_error: 0.0994\n",
      "\nEpoch 00198: val_loss did not improve from 0.09364\nEpoch 199/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0249 - mean_absolute_error: 0.0249",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......]",
      " - ETA: 0s - loss: 0.0301 - mean_absolute_error: 0.0301",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0306 - mean_absolute_error: 0.0306 - val_loss: 0.1498 - val_mean_absolute_error: 0.1498\n",
      "\nEpoch 00199: val_loss did not improve from 0.09364\nEpoch 200/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0308 - mean_absolute_error: 0.0308",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0290 - mean_absolute_error: 0.0290",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0286 - mean_absolute_error: 0.0286 - val_loss: 0.0955 - val_mean_absolute_error: 0.0955\n",
      "\nEpoch 00200: val_loss did not improve from 0.09364\nEpoch 201/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0291 - mean_absolute_error: 0.0291",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0305 - mean_absolute_error: 0.0305",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0294 - mean_absolute_error: 0.0294 - val_loss: 0.1114 - val_mean_absolute_error: 0.1114\n",
      "\nEpoch 00201: val_loss did not improve from 0.09364\nEpoch 202/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0187 - mean_absolute_error: 0.0187",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0280 - mean_absolute_error: 0.0280",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0279 - mean_absolute_error: 0.0279 - val_loss: 0.0989 - val_mean_absolute_error: 0.0989\n",
      "\nEpoch 00202: val_loss did not improve from 0.09364\nEpoch 203/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0334 - mean_absolute_error: 0.0334",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0285 - mean_absolute_error: 0.0285",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 65us/step - loss: 0.0290 - mean_absolute_error: 0.0290 - val_loss: 0.1087 - val_mean_absolute_error: 0.1087\n",
      "\nEpoch 00203: val_loss did not improve from 0.09364\nEpoch 204/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0302 - mean_absolute_error: 0.0302",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0281 - mean_absolute_error: 0.0281",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0277 - mean_absolute_error: 0.0277 - val_loss: 0.1067 - val_mean_absolute_error: 0.1067\n",
      "\nEpoch 00204: val_loss did not improve from 0.09364\nEpoch 205/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0312 - mean_absolute_error: 0.0312",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0284 - mean_absolute_error: 0.0284",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0270 - mean_absolute_error: 0.0270 - val_loss: 0.1599 - val_mean_absolute_error: 0.1599\n",
      "\nEpoch 00205: val_loss did not improve from 0.09364\nEpoch 206/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0335 - mean_absolute_error: 0.0335",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0268 - mean_absolute_error: 0.0268",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0257 - mean_absolute_error: 0.0257 - val_loss: 0.0989 - val_mean_absolute_error: 0.0989\n",
      "\nEpoch 00206: val_loss did not improve from 0.09364\nEpoch 207/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0237 - mean_absolute_error: 0.0237",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0268 - mean_absolute_error: 0.0268",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1417/1417 [==============================] - 0s 63us/step - loss: 0.0293 - mean_absolute_error: 0.0293 - val_loss: 0.1439 - val_mean_absolute_error: 0.1439\n",
      "\nEpoch 00207: val_loss did not improve from 0.09364\nEpoch 208/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0252 - mean_absolute_error: 0.0252",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 864/1417 [=================>............] - ETA: 0s - loss: 0.0382 - mean_absolute_error: 0.0382",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 62us/step - loss: 0.0397 - mean_absolute_error: 0.0397 - val_loss: 0.1111 - val_mean_absolute_error: 0.1111\n",
      "\nEpoch 00208: val_loss did not improve from 0.09364\nEpoch 209/500\n\r  32/1417 [..............................]",
      " - ETA: 0s - loss: 0.0412 - mean_absolute_error: 0.0412",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0312 - mean_absolute_error: 0.0312",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0299 - mean_absolute_error: 0.0299 - val_loss: 0.1034 - val_mean_absolute_error: 0.1034\n",
      "\nEpoch 00209: val_loss did not improve from 0.09364\nEpoch 210/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0239 - mean_absolute_error: 0.0239",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0311 - mean_absolute_error: 0.0311",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0317 - mean_absolute_error: 0.0317 - val_loss: 0.1073 - val_mean_absolute_error: 0.1073\n",
      "\nEpoch 00210: val_loss did not improve from 0.09364\nEpoch 211/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0343 - mean_absolute_error: 0.0343",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0269 - mean_absolute_error: 0.0269",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0259 - mean_absolute_error: 0.0259 - val_loss: 0.1036 - val_mean_absolute_error: 0.1036\n",
      "\nEpoch 00211: val_loss did not improve from 0.09364\nEpoch 212/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0275 - mean_absolute_error: 0.0275",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 864/1417 [=================>............] - ETA: 0s - loss: 0.0276 - mean_absolute_error: 0.0276",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0296 - mean_absolute_error: 0.0296 - val_loss: 0.1007 - val_mean_absolute_error: 0.1007\n",
      "\nEpoch 00212: val_loss did not improve from 0.09364\nEpoch 213/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0345 - mean_absolute_error: 0.0345",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0284 - mean_absolute_error: 0.0284",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 65us/step - loss: 0.0290 - mean_absolute_error: 0.0290 - val_loss: 0.1367 - val_mean_absolute_error: 0.1367\n",
      "\nEpoch 00213: val_loss did not improve from 0.09364\nEpoch 214/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0321 - mean_absolute_error: 0.0321",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0338 - mean_absolute_error: 0.0338",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0324 - mean_absolute_error: 0.0324 - val_loss: 0.0978 - val_mean_absolute_error: 0.0978\n",
      "\nEpoch 00214: val_loss did not improve from 0.09364\nEpoch 215/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0199 - mean_absolute_error: 0.0199",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0269 - mean_absolute_error: 0.0269",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0264 - mean_absolute_error: 0.0264 - val_loss: 0.0967 - val_mean_absolute_error: 0.0967\n",
      "\nEpoch 00215: val_loss did not improve from 0.09364\nEpoch 216/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0242 - mean_absolute_error: 0.0242",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0312 - mean_absolute_error: 0.0312",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0292 - mean_absolute_error: 0.0292 - val_loss: 0.1070 - val_mean_absolute_error: 0.1070\n",
      "\nEpoch 00216: val_loss did not improve from 0.09364\nEpoch 217/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0218 - mean_absolute_error: 0.0218",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 832/1417 [================>.............] - ETA: 0s - loss: 0.0293 - mean_absolute_error: 0.0293",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 73us/step - loss: 0.0281 - mean_absolute_error: 0.0281 - val_loss: 0.0966 - val_mean_absolute_error: 0.0966\n",
      "\nEpoch 00217: val_loss did not improve from 0.09364\nEpoch 218/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0321 - mean_absolute_error: 0.0321",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0301 - mean_absolute_error: 0.0301",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0306 - mean_absolute_error: 0.0306 - val_loss: 0.1238 - val_mean_absolute_error: 0.1238\n",
      "\nEpoch 00218: val_loss did not improve from 0.09364\nEpoch 219/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0321 - mean_absolute_error: 0.0321",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0312 - mean_absolute_error: 0.0312",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0330 - mean_absolute_error: 0.0330 - val_loss: 0.0948 - val_mean_absolute_error: 0.0948\n",
      "\nEpoch 00219: val_loss did not improve from 0.09364\nEpoch 220/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0331 - mean_absolute_error: 0.0331",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0279 - mean_absolute_error: 0.0279",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0270 - mean_absolute_error: 0.0270 - val_loss: 0.1213 - val_mean_absolute_error: 0.1213\n",
      "\nEpoch 00220: val_loss did not improve from 0.09364\nEpoch 221/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0274 - mean_absolute_error: 0.0274",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0296 - mean_absolute_error: 0.0296",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0284 - mean_absolute_error: 0.0284 - val_loss: 0.1542 - val_mean_absolute_error: 0.1542\n",
      "\nEpoch 00221: val_loss did not improve from 0.09364\nEpoch 222/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0438 - mean_absolute_error: 0.0438",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0379 - mean_absolute_error: 0.0379",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0358 - mean_absolute_error: 0.0358 - val_loss: 0.1074 - val_mean_absolute_error: 0.1074\n",
      "\nEpoch 00222: val_loss did not improve from 0.09364\nEpoch 223/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0251 - mean_absolute_error: 0.0251",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0259 - mean_absolute_error: 0.0259",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0251 - mean_absolute_error: 0.0251 - val_loss: 0.0953 - val_mean_absolute_error: 0.0953\n",
      "\nEpoch 00223: val_loss did not improve from 0.09364\nEpoch 224/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0243 - mean_absolute_error: 0.0243",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0245 - mean_absolute_error: 0.0245",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0250 - mean_absolute_error: 0.0250 - val_loss: 0.0970 - val_mean_absolute_error: 0.0970\n",
      "\nEpoch 00224: val_loss did not improve from 0.09364\nEpoch 225/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0247 - mean_absolute_error: 0.0247",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0284 - mean_absolute_error: 0.0284",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0280 - mean_absolute_error: 0.0280 - val_loss: 0.0976 - val_mean_absolute_error: 0.0976\n",
      "\nEpoch 00225: val_loss did not improve from 0.09364\nEpoch 226/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0245 - mean_absolute_error: 0.0245",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0262 - mean_absolute_error: 0.0262",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 65us/step - loss: 0.0264 - mean_absolute_error: 0.0264 - val_loss: 0.1353 - val_mean_absolute_error: 0.1353\n",
      "\nEpoch 00226: val_loss did not improve from 0.09364\nEpoch 227/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0377 - mean_absolute_error: 0.0377",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0414 - mean_absolute_error: 0.0414",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0373 - mean_absolute_error: 0.0373 - val_loss: 0.1131 - val_mean_absolute_error: 0.1131\n",
      "\nEpoch 00227: val_loss did not improve from 0.09364\nEpoch 228/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0283 - mean_absolute_error: 0.0283",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0268 - mean_absolute_error: 0.0268",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0264 - mean_absolute_error: 0.0264 - val_loss: 0.0968 - val_mean_absolute_error: 0.0968\n",
      "\nEpoch 00228: val_loss did not improve from 0.09364\nEpoch 229/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0200 - mean_absolute_error: 0.0200",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0263 - mean_absolute_error: 0.0263",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0258 - mean_absolute_error: 0.0258 - val_loss: 0.1019 - val_mean_absolute_error: 0.1019\n",
      "\nEpoch 00229: val_loss did not improve from 0.09364\nEpoch 230/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0168 - mean_absolute_error: 0.0168",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 832/1417 [================>.............] - ETA: 0s - loss: 0.0245 - mean_absolute_error: 0.0245",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 69us/step - loss: 0.0254 - mean_absolute_error: 0.0254 - val_loss: 0.1113 - val_mean_absolute_error: 0.1113\n",
      "\nEpoch 00230: val_loss did not improve from 0.09364\nEpoch 231/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0203 - mean_absolute_error: 0.0203",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0268 - mean_absolute_error: 0.0268",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0266 - mean_absolute_error: 0.0266 - val_loss: 0.1035 - val_mean_absolute_error: 0.1035\n",
      "\nEpoch 00231: val_loss did not improve from 0.09364\nEpoch 232/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0211 - mean_absolute_error: 0.0211",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0281 - mean_absolute_error: 0.0281",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0284 - mean_absolute_error: 0.0284 - val_loss: 0.1000 - val_mean_absolute_error: 0.1000\n",
      "\nEpoch 00232: val_loss did not improve from 0.09364\nEpoch 233/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0240 - mean_absolute_error: 0.0240",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 832/1417 [================>.............] - ETA: 0s - loss: 0.0314 - mean_absolute_error: 0.0314",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 74us/step - loss: 0.0299 - mean_absolute_error: 0.0299 - val_loss: 0.1226 - val_mean_absolute_error: 0.1226\n",
      "\nEpoch 00233: val_loss did not improve from 0.09364\nEpoch 234/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0273 - mean_absolute_error: 0.0273",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0273 - mean_absolute_error: 0.0273",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0280 - mean_absolute_error: 0.0280 - val_loss: 0.1045 - val_mean_absolute_error: 0.1045\n",
      "\nEpoch 00234: val_loss did not improve from 0.09364\nEpoch 235/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0120 - mean_absolute_error: 0.0120",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0254 - mean_absolute_error: 0.0254",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0257 - mean_absolute_error: 0.0257 - val_loss: 0.1037 - val_mean_absolute_error: 0.1037\n",
      "\nEpoch 00235: val_loss did not improve from 0.09364\nEpoch 236/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0221 - mean_absolute_error: 0.0221",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0306 - mean_absolute_error: 0.0306",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 71us/step - loss: 0.0293 - mean_absolute_error: 0.0293 - val_loss: 0.1112 - val_mean_absolute_error: 0.1112\n",
      "\nEpoch 00236: val_loss did not improve from 0.09364\nEpoch 237/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0182 - mean_absolute_error: 0.0182",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0263 - mean_absolute_error: 0.0263",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0254 - mean_absolute_error: 0.0254 - val_loss: 0.1074 - val_mean_absolute_error: 0.1074\n",
      "\nEpoch 00237: val_loss did not improve from 0.09364\nEpoch 238/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0235 - mean_absolute_error: 0.0235",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0320 - mean_absolute_error: 0.0320",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0311 - mean_absolute_error: 0.0311 - val_loss: 0.1042 - val_mean_absolute_error: 0.1042\n",
      "\nEpoch 00238: val_loss did not improve from 0.09364\nEpoch 239/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0273 - mean_absolute_error: 0.0273",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0301 - mean_absolute_error: 0.0301",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0307 - mean_absolute_error: 0.0307 - val_loss: 0.1551 - val_mean_absolute_error: 0.1551\n",
      "\nEpoch 00239: val_loss did not improve from 0.09364\nEpoch 240/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0299 - mean_absolute_error: 0.0299",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0305 - mean_absolute_error: 0.0305",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0320 - mean_absolute_error: 0.0320 - val_loss: 0.0973 - val_mean_absolute_error: 0.0973\n",
      "\nEpoch 00240: val_loss did not improve from 0.09364\nEpoch 241/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0334 - mean_absolute_error: 0.0334",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0282 - mean_absolute_error: 0.0282",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0290 - mean_absolute_error: 0.0290 - val_loss: 0.1073 - val_mean_absolute_error: 0.1073\n",
      "\nEpoch 00241: val_loss did not improve from 0.09364\nEpoch 242/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0206 - mean_absolute_error: 0.0206",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0279 - mean_absolute_error: 0.0279",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0274 - mean_absolute_error: 0.0274 - val_loss: 0.1036 - val_mean_absolute_error: 0.1036\n",
      "\nEpoch 00242: val_loss did not improve from 0.09364\nEpoch 243/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0213 - mean_absolute_error: 0.0213",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0261 - mean_absolute_error: 0.0261",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0263 - mean_absolute_error: 0.0263 - val_loss: 0.0978 - val_mean_absolute_error: 0.0978\n",
      "\nEpoch 00243: val_loss did not improve from 0.09364\nEpoch 244/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0291 - mean_absolute_error: 0.0291",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0302 - mean_absolute_error: 0.0302",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0307 - mean_absolute_error: 0.0307 - val_loss: 0.0973 - val_mean_absolute_error: 0.0973\n",
      "\nEpoch 00244: val_loss did not improve from 0.09364\nEpoch 245/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0283 - mean_absolute_error: 0.0283",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0262 - mean_absolute_error: 0.0262",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0270 - mean_absolute_error: 0.0270 - val_loss: 0.1015 - val_mean_absolute_error: 0.1015\n",
      "\nEpoch 00245: val_loss did not improve from 0.09364\nEpoch 246/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0356 - mean_absolute_error: 0.0356",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0267 - mean_absolute_error: 0.0267",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 65us/step - loss: 0.0260 - mean_absolute_error: 0.0260 - val_loss: 0.1104 - val_mean_absolute_error: 0.1104\n",
      "\nEpoch 00246: val_loss did not improve from 0.09364\nEpoch 247/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0331 - mean_absolute_error: 0.0331",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0263 - mean_absolute_error: 0.0263",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0269 - mean_absolute_error: 0.0269 - val_loss: 0.0986 - val_mean_absolute_error: 0.0986\n",
      "\nEpoch 00247: val_loss did not improve from 0.09364\nEpoch 248/500",
      "\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0390 - mean_absolute_error: 0.0390",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0296 - mean_absolute_error: 0.0296",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0283 - mean_absolute_error: 0.0283 - val_loss: 0.1106 - val_mean_absolute_error: 0.1106\n",
      "\nEpoch 00248: val_loss did not improve from 0.09364\nEpoch 249/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0268 - mean_absolute_error: 0.0268",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0293 - mean_absolute_error: 0.0293",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0282 - mean_absolute_error: 0.0282 - val_loss: 0.1037 - val_mean_absolute_error: 0.1037\n",
      "\nEpoch 00249: val_loss did not improve from 0.09364\n",
      "Epoch 250/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0305 - mean_absolute_error: 0.0305",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0253 - mean_absolute_error: 0.0253",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0247 - mean_absolute_error: 0.0247 - val_loss: 0.1029 - val_mean_absolute_error: 0.1029\n",
      "\nEpoch 00250: val_loss did not improve from 0.09364\nEpoch 251/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0182 - mean_absolute_error: 0.0182",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0238 - mean_absolute_error: 0.0238",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 76us/step - loss: 0.0251 - mean_absolute_error: 0.0251 - val_loss: 0.1226 - val_mean_absolute_error: 0.1226\n",
      "\nEpoch 00251: val_loss did not improve from 0.09364\nEpoch 252/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0257 - mean_absolute_error: 0.0257",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 832/1417 [================>.............] - ETA: 0s - loss: 0.0274 - mean_absolute_error: 0.0274",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 71us/step - loss: 0.0293 - mean_absolute_error: 0.0293 - val_loss: 0.1906 - val_mean_absolute_error: 0.1906\n",
      "\nEpoch 00252: val_loss did not improve from 0.09364\nEpoch 253/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0335 - mean_absolute_error: 0.0335",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0344 - mean_absolute_error: 0.0344",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 68us/step - loss: 0.0329 - mean_absolute_error: 0.0329 - val_loss: 0.1186 - val_mean_absolute_error: 0.1186\n",
      "\nEpoch 00253: val_loss did not improve from 0.09364\nEpoch 254/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0341 - mean_absolute_error: 0.0341",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0272 - mean_absolute_error: 0.0272",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0260 - mean_absolute_error: 0.0260 - val_loss: 0.1004 - val_mean_absolute_error: 0.1004\n",
      "\nEpoch 00254: val_loss did not improve from 0.09364\nEpoch 255/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0269 - mean_absolute_error: 0.0269",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0250 - mean_absolute_error: 0.0250",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0247 - mean_absolute_error: 0.0247 - val_loss: 0.0979 - val_mean_absolute_error: 0.0979\n",
      "\nEpoch 00255: val_loss did not improve from 0.09364\nEpoch 256/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0226 - mean_absolute_error: 0.0226",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0242 - mean_absolute_error: 0.0242",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 62us/step - loss: 0.0237 - mean_absolute_error: 0.0237 - val_loss: 0.1004 - val_mean_absolute_error: 0.1004\n",
      "\nEpoch 00256: val_loss did not improve from 0.09364\nEpoch 257/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0275 - mean_absolute_error: 0.0275",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0248 - mean_absolute_error: 0.0248",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0258 - mean_absolute_error: 0.0258 - val_loss: 0.0964 - val_mean_absolute_error: 0.0964\n",
      "\nEpoch 00257: val_loss did not improve from 0.09364\nEpoch 258/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0195 - mean_absolute_error: 0.0195",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0293 - mean_absolute_error: 0.0293",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 68us/step - loss: 0.0309 - mean_absolute_error: 0.0309 - val_loss: 0.1452 - val_mean_absolute_error: 0.1452\n",
      "\nEpoch 00258: val_loss did not improve from 0.09364\nEpoch 259/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0519 - mean_absolute_error: 0.0519",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0339 - mean_absolute_error: 0.0339",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0306 - mean_absolute_error: 0.0306 - val_loss: 0.0949 - val_mean_absolute_error: 0.0949\n",
      "\nEpoch 00259: val_loss did not improve from 0.09364\nEpoch 260/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0306 - mean_absolute_error: 0.0306",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0282 - mean_absolute_error: 0.0282",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0281 - mean_absolute_error: 0.0281 - val_loss: 0.1059 - val_mean_absolute_error: 0.1059\n",
      "\nEpoch 00260: val_loss did not improve from 0.09364\nEpoch 261/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0253 - mean_absolute_error: 0.0253",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0266 - mean_absolute_error: 0.0266",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0287 - mean_absolute_error: 0.0287 - val_loss: 0.1245 - val_mean_absolute_error: 0.1245\n",
      "\nEpoch 00261: val_loss did not improve from 0.09364\nEpoch 262/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0387 - mean_absolute_error: 0.0387",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0270 - mean_absolute_error: 0.0270",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0272 - mean_absolute_error: 0.0272 - val_loss: 0.1251 - val_mean_absolute_error: 0.1251\n",
      "\nEpoch 00262: val_loss did not improve from 0.09364\nEpoch 263/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0472 - mean_absolute_error: 0.0472",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 800/1417 [===============>..............] - ETA: 0s - loss: 0.0332 - mean_absolute_error: 0.0332",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 67us/step - loss: 0.0319 - mean_absolute_error: 0.0319 - val_loss: 0.1175 - val_mean_absolute_error: 0.1175\n",
      "\nEpoch 00263: val_loss did not improve from 0.09364\nEpoch 264/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0376 - mean_absolute_error: 0.0376",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0259 - mean_absolute_error: 0.0259",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0254 - mean_absolute_error: 0.0254 - val_loss: 0.0951 - val_mean_absolute_error: 0.0951\n",
      "\nEpoch 00264: val_loss did not improve from 0.09364\nEpoch 265/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0212 - mean_absolute_error: 0.0212",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0275 - mean_absolute_error: 0.0275",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0286 - mean_absolute_error: 0.0286 - val_loss: 0.0998 - val_mean_absolute_error: 0.0998\n",
      "\nEpoch 00265: val_loss did not improve from 0.09364\nEpoch 266/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0286 - mean_absolute_error: 0.0286",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0253 - mean_absolute_error: 0.0253",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0244 - mean_absolute_error: 0.0244 - val_loss: 0.0977 - val_mean_absolute_error: 0.0977\n",
      "\nEpoch 00266: val_loss did not improve from 0.09364\nEpoch 267/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0219 - mean_absolute_error: 0.0219",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0230 - mean_absolute_error: 0.0230",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0251 - mean_absolute_error: 0.0251 - val_loss: 0.1039 - val_mean_absolute_error: 0.1039\n",
      "\nEpoch 00267: val_loss did not improve from 0.09364\nEpoch 268/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0195 - mean_absolute_error: 0.0195",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0313 - mean_absolute_error: 0.0313",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0300 - mean_absolute_error: 0.0300 - val_loss: 0.0973 - val_mean_absolute_error: 0.0973\n",
      "\nEpoch 00268: val_loss did not improve from 0.09364\nEpoch 269/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0213 - mean_absolute_error: 0.0213",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0245 - mean_absolute_error: 0.0245",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0254 - mean_absolute_error: 0.0254 - val_loss: 0.0977 - val_mean_absolute_error: 0.0977\n",
      "\nEpoch 00269: val_loss did not improve from 0.09364\nEpoch 270/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0157 - mean_absolute_error: 0.0157",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0255 - mean_absolute_error: 0.0255",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0254 - mean_absolute_error: 0.0254 - val_loss: 0.1097 - val_mean_absolute_error: 0.1097\n",
      "\nEpoch 00270: val_loss did not improve from 0.09364\nEpoch 271/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0461 - mean_absolute_error: 0.0461",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0330 - mean_absolute_error: 0.0330",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0340 - mean_absolute_error: 0.0340 - val_loss: 0.1182 - val_mean_absolute_error: 0.1182\n",
      "\nEpoch 00271: val_loss did not improve from 0.09364\n",
      "Epoch 272/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0528 - mean_absolute_error: 0.0528",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0320 - mean_absolute_error: 0.0320",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0305 - mean_absolute_error: 0.0305 - val_loss: 0.1047 - val_mean_absolute_error: 0.1047\n",
      "\nEpoch 00272: val_loss did not improve from 0.09364\nEpoch 273/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0329 - mean_absolute_error: 0.0329",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0249 - mean_absolute_error: 0.0249",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0250 - mean_absolute_error: 0.0250 - val_loss: 0.1126 - val_mean_absolute_error: 0.1126\n",
      "\nEpoch 00273: val_loss did not improve from 0.09364\nEpoch 274/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0294 - mean_absolute_error: 0.0294",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0330 - mean_absolute_error: 0.0330",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0297 - mean_absolute_error: 0.0297 - val_loss: 0.0999 - val_mean_absolute_error: 0.0999\n",
      "\nEpoch 00274: val_loss did not improve from 0.09364\nEpoch 275/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0159 - mean_absolute_error: 0.0159",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0216 - mean_absolute_error: 0.0216",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0211 - mean_absolute_error: 0.0211 - val_loss: 0.0948 - val_mean_absolute_error: 0.0948\n",
      "\nEpoch 00275: val_loss did not improve from 0.09364\nEpoch 276/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0205 - mean_absolute_error: 0.0205",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0213 - mean_absolute_error: 0.0213",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 67us/step - loss: 0.0218 - mean_absolute_error: 0.0218 - val_loss: 0.0994 - val_mean_absolute_error: 0.0994\n",
      "\nEpoch 00276: val_loss did not improve from 0.09364\nEpoch 277/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0295 - mean_absolute_error: 0.0295",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0261 - mean_absolute_error: 0.0261",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0256 - mean_absolute_error: 0.0256 - val_loss: 0.1079 - val_mean_absolute_error: 0.1079\n",
      "\nEpoch 00277: val_loss did not improve from 0.09364\nEpoch 278/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0243 - mean_absolute_error: 0.0243",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........]",
      " - ETA: 0s - loss: 0.0234 - mean_absolute_error: 0.0234",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0246 - mean_absolute_error: 0.0246 - val_loss: 0.1200 - val_mean_absolute_error: 0.1200\n",
      "\nEpoch 00278: val_loss did not improve from 0.09364\nEpoch 279/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0370 - mean_absolute_error: 0.0370",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0253 - mean_absolute_error: 0.0253",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0252 - mean_absolute_error: 0.0252 - val_loss: 0.0972 - val_mean_absolute_error: 0.0972\n",
      "\nEpoch 00279: val_loss did not improve from 0.09364\nEpoch 280/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0205 - mean_absolute_error: 0.0205",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0257 - mean_absolute_error: 0.0257",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0265 - mean_absolute_error: 0.0265 - val_loss: 0.1389 - val_mean_absolute_error: 0.1389\n",
      "\nEpoch 00280: val_loss did not improve from 0.09364\n",
      "Epoch 281/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0287 - mean_absolute_error: 0.0287",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0250 - mean_absolute_error: 0.0250",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0256 - mean_absolute_error: 0.0256 - val_loss: 0.1139 - val_mean_absolute_error: 0.1139\n",
      "\nEpoch 00281: val_loss did not improve from 0.09364\nEpoch 282/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0501 - mean_absolute_error: 0.0501",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0332 - mean_absolute_error: 0.0332",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0315 - mean_absolute_error: 0.0315 - val_loss: 0.1120 - val_mean_absolute_error: 0.1120\n",
      "\nEpoch 00282: val_loss did not improve from 0.09364\nEpoch 283/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0226 - mean_absolute_error: 0.0226",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0211 - mean_absolute_error: 0.0211",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0213 - mean_absolute_error: 0.0213 - val_loss: 0.1040 - val_mean_absolute_error: 0.1040\n",
      "\nEpoch 00283: val_loss did not improve from 0.09364\nEpoch 284/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0266 - mean_absolute_error: 0.0266",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0302 - mean_absolute_error: 0.0302",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0289 - mean_absolute_error: 0.0289 - val_loss: 0.1019 - val_mean_absolute_error: 0.1019\n",
      "\nEpoch 00284: val_loss did not improve from 0.09364\nEpoch 285/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0225 - mean_absolute_error: 0.0225",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0271 - mean_absolute_error: 0.0271",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0264 - mean_absolute_error: 0.0264 - val_loss: 0.0963 - val_mean_absolute_error: 0.0963\n",
      "\nEpoch 00285: val_loss did not improve from 0.09364\nEpoch 286/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0155 - mean_absolute_error: 0.0155",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0228 - mean_absolute_error: 0.0228",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 62us/step - loss: 0.0232 - mean_absolute_error: 0.0232 - val_loss: 0.1234 - val_mean_absolute_error: 0.1234\n",
      "\nEpoch 00286: val_loss did not improve from 0.09364\nEpoch 287/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0242 - mean_absolute_error: 0.0242",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0273 - mean_absolute_error: 0.0273",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0272 - mean_absolute_error: 0.0272 - val_loss: 0.1001 - val_mean_absolute_error: 0.1001\n",
      "\nEpoch 00287: val_loss did not improve from 0.09364\nEpoch 288/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0320 - mean_absolute_error: 0.0320",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0242 - mean_absolute_error: 0.0242",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0241 - mean_absolute_error: 0.0241 - val_loss: 0.1076 - val_mean_absolute_error: 0.1076\n",
      "\nEpoch 00288: val_loss did not improve from 0.09364\nEpoch 289/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0279 - mean_absolute_error: 0.0279",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0271 - mean_absolute_error: 0.0271",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0262 - mean_absolute_error: 0.0262 - val_loss: 0.1161 - val_mean_absolute_error: 0.1161\n",
      "\nEpoch 00289: val_loss did not improve from 0.09364\nEpoch 290/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0280 - mean_absolute_error: 0.0280",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0273 - mean_absolute_error: 0.0273",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0257 - mean_absolute_error: 0.0257 - val_loss: 0.1174 - val_mean_absolute_error: 0.1174\n",
      "\nEpoch 00290: val_loss did not improve from 0.09364\nEpoch 291/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0231 - mean_absolute_error: 0.0231",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0342 - mean_absolute_error: 0.0342",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0315 - mean_absolute_error: 0.0315 - val_loss: 0.0977 - val_mean_absolute_error: 0.0977\n",
      "\nEpoch 00291: val_loss did not improve from 0.09364\nEpoch 292/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0105 - mean_absolute_error: 0.0105",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0213 - mean_absolute_error: 0.0213",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0220 - mean_absolute_error: 0.0220 - val_loss: 0.1032 - val_mean_absolute_error: 0.1032\n",
      "\nEpoch 00292: val_loss did not improve from 0.09364\nEpoch 293/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0171 - mean_absolute_error: 0.0171",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0265 - mean_absolute_error: 0.0265",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 65us/step - loss: 0.0261 - mean_absolute_error: 0.0261 - val_loss: 0.1110 - val_mean_absolute_error: 0.1110\n",
      "\nEpoch 00293: val_loss did not improve from 0.09364\nEpoch 294/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0179 - mean_absolute_error: 0.0179",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0321 - mean_absolute_error: 0.0321",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0309 - mean_absolute_error: 0.0309 - val_loss: 0.1023 - val_mean_absolute_error: 0.1023\n",
      "\nEpoch 00294: val_loss did not improve from 0.09364\nEpoch 295/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0277 - mean_absolute_error: 0.0277",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0301 - mean_absolute_error: 0.0301",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0293 - mean_absolute_error: 0.0293 - val_loss: 0.1012 - val_mean_absolute_error: 0.1012\n",
      "\nEpoch 00295: val_loss did not improve from 0.09364\nEpoch 296/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0503 - mean_absolute_error: 0.0503",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0258 - mean_absolute_error: 0.0258",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 62us/step - loss: 0.0250 - mean_absolute_error: 0.0250 - val_loss: 0.1143 - val_mean_absolute_error: 0.1143\n",
      "\nEpoch 00296: val_loss did not improve from 0.09364\nEpoch 297/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0296 - mean_absolute_error: 0.0296",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0270 - mean_absolute_error: 0.0270",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0258 - mean_absolute_error: 0.0258 - val_loss: 0.1078 - val_mean_absolute_error: 0.1078\n",
      "\nEpoch 00297: val_loss did not improve from 0.09364\nEpoch 298/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0328 - mean_absolute_error: 0.0328",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0362 - mean_absolute_error: 0.0362",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0319 - mean_absolute_error: 0.0319 - val_loss: 0.1089 - val_mean_absolute_error: 0.1089\n",
      "\nEpoch 00298: val_loss did not improve from 0.09364\nEpoch 299/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0257 - mean_absolute_error: 0.0257",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0252 - mean_absolute_error: 0.0252",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0241 - mean_absolute_error: 0.0241 - val_loss: 0.1007 - val_mean_absolute_error: 0.1007\n",
      "\nEpoch 00299: val_loss did not improve from 0.09364\nEpoch 300/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0226 - mean_absolute_error: 0.0226",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0311 - mean_absolute_error: 0.0311",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0308 - mean_absolute_error: 0.0308 - val_loss: 0.0966 - val_mean_absolute_error: 0.0966\n",
      "\nEpoch 00300: val_loss did not improve from 0.09364\nEpoch 301/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0210 - mean_absolute_error: 0.0210",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0281 - mean_absolute_error: 0.0281",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 68us/step - loss: 0.0265 - mean_absolute_error: 0.0265 - val_loss: 0.1032 - val_mean_absolute_error: 0.1032\n",
      "\nEpoch 00301: val_loss did not improve from 0.09364\nEpoch 302/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0185 - mean_absolute_error: 0.0185",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0229 - mean_absolute_error: 0.0229",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0224 - mean_absolute_error: 0.0224 - val_loss: 0.0992 - val_mean_absolute_error: 0.0992\n",
      "\nEpoch 00302: val_loss did not improve from 0.09364\nEpoch 303/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0250 - mean_absolute_error: 0.0250",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0266 - mean_absolute_error: 0.0266",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0255 - mean_absolute_error: 0.0255 - val_loss: 0.1114 - val_mean_absolute_error: 0.1114\n",
      "\nEpoch 00303: val_loss did not improve from 0.09364\nEpoch 304/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0309 - mean_absolute_error: 0.0309",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0233 - mean_absolute_error: 0.0233",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0247 - mean_absolute_error: 0.0247 - val_loss: 0.1086 - val_mean_absolute_error: 0.1086\n",
      "\nEpoch 00304: val_loss did not improve from 0.09364\nEpoch 305/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0318 - mean_absolute_error: 0.0318",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0253 - mean_absolute_error: 0.0253",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0252 - mean_absolute_error: 0.0252 - val_loss: 0.1056 - val_mean_absolute_error: 0.1056\n",
      "\nEpoch 00305: val_loss did not improve from 0.09364\nEpoch 306/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0401 - mean_absolute_error: 0.0401",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0244 - mean_absolute_error: 0.0244",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0247 - mean_absolute_error: 0.0247 - val_loss: 0.1164 - val_mean_absolute_error: 0.1164\n",
      "\nEpoch 00306: val_loss did not improve from 0.09364\nEpoch 307/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0284 - mean_absolute_error: 0.0284",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0253 - mean_absolute_error: 0.0253",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0248 - mean_absolute_error: 0.0248 - val_loss: 0.1015 - val_mean_absolute_error: 0.1015\n",
      "\nEpoch 00307: val_loss did not improve from 0.09364\nEpoch 308/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0221 - mean_absolute_error: 0.0221",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0295 - mean_absolute_error: 0.0295",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0278 - mean_absolute_error: 0.0278 - val_loss: 0.1047 - val_mean_absolute_error: 0.1047\n",
      "\nEpoch 00308: val_loss did not improve from 0.09364\nEpoch 309/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0166 - mean_absolute_error: 0.0166",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0255 - mean_absolute_error: 0.0255",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0257 - mean_absolute_error: 0.0257 - val_loss: 0.1154 - val_mean_absolute_error: 0.1154\n",
      "\nEpoch 00309: val_loss did not improve from 0.09364\nEpoch 310/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0296 - mean_absolute_error: 0.0296",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0225 - mean_absolute_error: 0.0225",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0216 - mean_absolute_error: 0.0216 - val_loss: 0.1046 - val_mean_absolute_error: 0.1046\n",
      "\nEpoch 00310: val_loss did not improve from 0.09364\nEpoch 311/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0181 - mean_absolute_error: 0.0181",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0243 - mean_absolute_error: 0.0243",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0239 - mean_absolute_error: 0.0239 - val_loss: 0.1070 - val_mean_absolute_error: 0.1070\n",
      "\nEpoch 00311: val_loss did not improve from 0.09364\nEpoch 312/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0193 - mean_absolute_error: 0.0193",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0231 - mean_absolute_error: 0.0231",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0237 - mean_absolute_error: 0.0237 - val_loss: 0.0975 - val_mean_absolute_error: 0.0975\n",
      "\nEpoch 00312: val_loss did not improve from 0.09364\nEpoch 313/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0248 - mean_absolute_error: 0.0248",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0253 - mean_absolute_error: 0.0253",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0242 - mean_absolute_error: 0.0242 - val_loss: 0.1020 - val_mean_absolute_error: 0.1020\n",
      "\nEpoch 00313: val_loss did not improve from 0.09364\nEpoch 314/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0216 - mean_absolute_error: 0.0216",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0248 - mean_absolute_error: 0.0248",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0266 - mean_absolute_error: 0.0266 - val_loss: 0.0982 - val_mean_absolute_error: 0.0982\n",
      "\nEpoch 00314: val_loss did not improve from 0.09364\nEpoch 315/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0305 - mean_absolute_error: 0.0305",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0266 - mean_absolute_error: 0.0266",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0264 - mean_absolute_error: 0.0264 - val_loss: 0.1337 - val_mean_absolute_error: 0.1337\n",
      "\nEpoch 00315: val_loss did not improve from 0.09364\nEpoch 316/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0379 - mean_absolute_error: 0.0379",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0232 - mean_absolute_error: 0.0232",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0238 - mean_absolute_error: 0.0238 - val_loss: 0.1331 - val_mean_absolute_error: 0.1331\n",
      "\nEpoch 00316: val_loss did not improve from 0.09364\nEpoch 317/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0226 - mean_absolute_error: 0.0226",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0218 - mean_absolute_error: 0.0218",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0216 - mean_absolute_error: 0.0216 - val_loss: 0.1036 - val_mean_absolute_error: 0.1036\n",
      "\nEpoch 00317: val_loss did not improve from 0.09364\nEpoch 318/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0242 - mean_absolute_error: 0.0242",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0237 - mean_absolute_error: 0.0237",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0234 - mean_absolute_error: 0.0234 - val_loss: 0.0999 - val_mean_absolute_error: 0.0999\n",
      "\nEpoch 00318: val_loss did not improve from 0.09364\nEpoch 319/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0405 - mean_absolute_error: 0.0405",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0234 - mean_absolute_error: 0.0234",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 64us/step - loss: 0.0227 - mean_absolute_error: 0.0227 - val_loss: 0.1014 - val_mean_absolute_error: 0.1014\n",
      "\nEpoch 00319: val_loss did not improve from 0.09364\nEpoch 320/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0294 - mean_absolute_error: 0.0294",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0214 - mean_absolute_error: 0.0214",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0209 - mean_absolute_error: 0.0209 - val_loss: 0.1029 - val_mean_absolute_error: 0.1029\n",
      "\nEpoch 00320: val_loss did not improve from 0.09364\nEpoch 321/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0193 - mean_absolute_error: 0.0193",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 864/1417 [=================>............] - ETA: 0s - loss: 0.0228 - mean_absolute_error: 0.0228",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 71us/step - loss: 0.0236 - mean_absolute_error: 0.0236 - val_loss: 0.1052 - val_mean_absolute_error: 0.1052\n",
      "\nEpoch 00321: val_loss did not improve from 0.09364\nEpoch 322/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0242 - mean_absolute_error: 0.0242",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0256 - mean_absolute_error: 0.0256",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0260 - mean_absolute_error: 0.0260 - val_loss: 0.1301 - val_mean_absolute_error: 0.1301\n",
      "\nEpoch 00322: val_loss did not improve from 0.09364\nEpoch 323/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0199 - mean_absolute_error: 0.0199",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0314 - mean_absolute_error: 0.0314",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 68us/step - loss: 0.0298 - mean_absolute_error: 0.0298 - val_loss: 0.1288 - val_mean_absolute_error: 0.1288\n",
      "\nEpoch 00323: val_loss did not improve from 0.09364\nEpoch 324/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0341 - mean_absolute_error: 0.0341",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0234 - mean_absolute_error: 0.0234",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0251 - mean_absolute_error: 0.0251 - val_loss: 0.1300 - val_mean_absolute_error: 0.1300\n",
      "\nEpoch 00324: val_loss did not improve from 0.09364\nEpoch 325/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0204 - mean_absolute_error: 0.0204",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0251 - mean_absolute_error: 0.0251",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0277 - mean_absolute_error: 0.0277 - val_loss: 0.1116 - val_mean_absolute_error: 0.1116\n",
      "\nEpoch 00325: val_loss did not improve from 0.09364",
      "\nEpoch 326/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0271 - mean_absolute_error: 0.0271",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0269 - mean_absolute_error: 0.0269",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0264 - mean_absolute_error: 0.0264 - val_loss: 0.1084 - val_mean_absolute_error: 0.1084\n",
      "\nEpoch 00326: val_loss did not improve from 0.09364\nEpoch 327/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0258 - mean_absolute_error: 0.0258",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0231 - mean_absolute_error: 0.0231",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0245 - mean_absolute_error: 0.0245 - val_loss: 0.1343 - val_mean_absolute_error: 0.1343\n",
      "\nEpoch 00327: val_loss did not improve from 0.09364\nEpoch 328/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0237 - mean_absolute_error: 0.0237",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0260 - mean_absolute_error: 0.0260",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0248 - mean_absolute_error: 0.0248 - val_loss: 0.1059 - val_mean_absolute_error: 0.1059\n",
      "\nEpoch 00328: val_loss did not improve from 0.09364\nEpoch 329/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0243 - mean_absolute_error: 0.0243",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0236 - mean_absolute_error: 0.0236",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0283 - mean_absolute_error: 0.0283 - val_loss: 0.1011 - val_mean_absolute_error: 0.1011\n",
      "\nEpoch 00329: val_loss did not improve from 0.09364\nEpoch 330/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0403 - mean_absolute_error: 0.0403",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0240 - mean_absolute_error: 0.0240",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0232 - mean_absolute_error: 0.0232 - val_loss: 0.1048 - val_mean_absolute_error: 0.1048\n",
      "\nEpoch 00330: val_loss did not improve from 0.09364\nEpoch 331/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0241 - mean_absolute_error: 0.0241",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0293 - mean_absolute_error: 0.0293",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0284 - mean_absolute_error: 0.0284 - val_loss: 0.1171 - val_mean_absolute_error: 0.1171\n",
      "\nEpoch 00331: val_loss did not improve from 0.09364\nEpoch 332/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0274 - mean_absolute_error: 0.0274",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0306 - mean_absolute_error: 0.0306",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0295 - mean_absolute_error: 0.0295 - val_loss: 0.1154 - val_mean_absolute_error: 0.1154\n",
      "\nEpoch 00332: val_loss did not improve from 0.09364\nEpoch 333/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0359 - mean_absolute_error: 0.0359",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0275 - mean_absolute_error: 0.0275",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0275 - mean_absolute_error: 0.0275 - val_loss: 0.1190 - val_mean_absolute_error: 0.1190\n",
      "\nEpoch 00333: val_loss did not improve from 0.09364\nEpoch 334/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0277 - mean_absolute_error: 0.0277",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0225 - mean_absolute_error: 0.0225",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0225 - mean_absolute_error: 0.0225 - val_loss: 0.0964 - val_mean_absolute_error: 0.0964\n",
      "\nEpoch 00334: val_loss did not improve from 0.09364\nEpoch 335/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0247 - mean_absolute_error: 0.0247",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0221 - mean_absolute_error: 0.0221",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0238 - mean_absolute_error: 0.0238 - val_loss: 0.1122 - val_mean_absolute_error: 0.1122\n",
      "\nEpoch 00335: val_loss did not improve from 0.09364\nEpoch 336/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0203 - mean_absolute_error: 0.0203",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0211 - mean_absolute_error: 0.0211",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0215 - mean_absolute_error: 0.0215 - val_loss: 0.1110 - val_mean_absolute_error: 0.1110\n",
      "\nEpoch 00336: val_loss did not improve from 0.09364\nEpoch 337/500",
      "\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0345 - mean_absolute_error: 0.0345",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0233 - mean_absolute_error: 0.0233",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0238 - mean_absolute_error: 0.0238 - val_loss: 0.1105 - val_mean_absolute_error: 0.1105\n",
      "\nEpoch 00337: val_loss did not improve from 0.09364\nEpoch 338/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0313 - mean_absolute_error: 0.0313",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0243 - mean_absolute_error: 0.0243",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0233 - mean_absolute_error: 0.0233 - val_loss: 0.1070 - val_mean_absolute_error: 0.1070\n",
      "\nEpoch 00338: val_loss did not improve from 0.09364\nEpoch 339/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0192 - mean_absolute_error: 0.0192",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0204 - mean_absolute_error: 0.0204",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0206 - mean_absolute_error: 0.0206 - val_loss: 0.1028 - val_mean_absolute_error: 0.1028\n",
      "\nEpoch 00339: val_loss did not improve from 0.09364\nEpoch 340/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0185 - mean_absolute_error: 0.0185",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0221 - mean_absolute_error: 0.0221",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0224 - mean_absolute_error: 0.0224 - val_loss: 0.1170 - val_mean_absolute_error: 0.1170\n",
      "\nEpoch 00340: val_loss did not improve from 0.09364\nEpoch 341/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0257 - mean_absolute_error: 0.0257",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0206 - mean_absolute_error: 0.0206",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0230 - mean_absolute_error: 0.0230 - val_loss: 0.1112 - val_mean_absolute_error: 0.1112\n",
      "\nEpoch 00341: val_loss did not improve from 0.09364\nEpoch 342/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0270 - mean_absolute_error: 0.0270",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0243 - mean_absolute_error: 0.0243",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0237 - mean_absolute_error: 0.0237 - val_loss: 0.1113 - val_mean_absolute_error: 0.1113\n",
      "\nEpoch 00342: val_loss did not improve from 0.09364\nEpoch 343/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0328 - mean_absolute_error: 0.0328",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 864/1417 [=================>............] - ETA: 0s - loss: 0.0266 - mean_absolute_error: 0.0266",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 69us/step - loss: 0.0241 - mean_absolute_error: 0.0241 - val_loss: 0.1077 - val_mean_absolute_error: 0.1077\n",
      "\nEpoch 00343: val_loss did not improve from 0.09364\nEpoch 344/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0144 - mean_absolute_error: 0.0144",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 864/1417 [=================>............] - ETA: 0s - loss: 0.0217 - mean_absolute_error: 0.0217",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 67us/step - loss: 0.0227 - mean_absolute_error: 0.0227 - val_loss: 0.1132 - val_mean_absolute_error: 0.1132\n",
      "\nEpoch 00344: val_loss did not improve from 0.09364\nEpoch 345/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0230 - mean_absolute_error: 0.0230",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0222 - mean_absolute_error: 0.0222",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0224 - mean_absolute_error: 0.0224 - val_loss: 0.1046 - val_mean_absolute_error: 0.1046\n",
      "\nEpoch 00345: val_loss did not improve from 0.09364\nEpoch 346/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0178 - mean_absolute_error: 0.0178",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0209 - mean_absolute_error: 0.0209",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 66us/step - loss: 0.0261 - mean_absolute_error: 0.0261 - val_loss: 0.1067 - val_mean_absolute_error: 0.1067\n",
      "\nEpoch 00346: val_loss did not improve from 0.09364\nEpoch 347/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0210 - mean_absolute_error: 0.0210",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0249 - mean_absolute_error: 0.0249",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0245 - mean_absolute_error: 0.0245 - val_loss: 0.1049 - val_mean_absolute_error: 0.1049\n",
      "\nEpoch 00347: val_loss did not improve from 0.09364\nEpoch 348/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0185 - mean_absolute_error: 0.0185",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0307 - mean_absolute_error: 0.0307",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0305 - mean_absolute_error: 0.0305 - val_loss: 0.1011 - val_mean_absolute_error: 0.1011\n",
      "\nEpoch 00348: val_loss did not improve from 0.09364\nEpoch 349/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0231 - mean_absolute_error: 0.0231",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0300 - mean_absolute_error: 0.0300",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0275 - mean_absolute_error: 0.0275 - val_loss: 0.1003 - val_mean_absolute_error: 0.1003\n",
      "\nEpoch 00349: val_loss did not improve from 0.09364\nEpoch 350/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0248 - mean_absolute_error: 0.0248",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0277 - mean_absolute_error: 0.0277",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 73us/step - loss: 0.0286 - mean_absolute_error: 0.0286 - val_loss: 0.1184 - val_mean_absolute_error: 0.1184\n",
      "\nEpoch 00350: val_loss did not improve from 0.09364\nEpoch 351/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0406 - mean_absolute_error: 0.0406",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0388 - mean_absolute_error: 0.0388",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0365 - mean_absolute_error: 0.0365 - val_loss: 0.1024 - val_mean_absolute_error: 0.1024\n",
      "\nEpoch 00351: val_loss did not improve from 0.09364\nEpoch 352/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0185 - mean_absolute_error: 0.0185",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0218 - mean_absolute_error: 0.0218",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0227 - mean_absolute_error: 0.0227 - val_loss: 0.1093 - val_mean_absolute_error: 0.1093\n",
      "\nEpoch 00352: val_loss did not improve from 0.09364\nEpoch 353/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0180 - mean_absolute_error: 0.0180",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0236 - mean_absolute_error: 0.0236",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0259 - mean_absolute_error: 0.0259 - val_loss: 0.1242 - val_mean_absolute_error: 0.1242\n",
      "\nEpoch 00353: val_loss did not improve from 0.09364\nEpoch 354/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0211 - mean_absolute_error: 0.0211",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0260 - mean_absolute_error: 0.0260",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0266 - mean_absolute_error: 0.0266 - val_loss: 0.1246 - val_mean_absolute_error: 0.1246\n",
      "\nEpoch 00354: val_loss did not improve from 0.09364\nEpoch 355/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0326 - mean_absolute_error: 0.0326",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0254 - mean_absolute_error: 0.0254",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0255 - mean_absolute_error: 0.0255 - val_loss: 0.1028 - val_mean_absolute_error: 0.1028\n",
      "\nEpoch 00355: val_loss did not improve from 0.09364\nEpoch 356/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0226 - mean_absolute_error: 0.0226",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0239 - mean_absolute_error: 0.0239",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0239 - mean_absolute_error: 0.0239 - val_loss: 0.1023 - val_mean_absolute_error: 0.1023\n",
      "\nEpoch 00356: val_loss did not improve from 0.09364\nEpoch 357/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0217 - mean_absolute_error: 0.0217",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0236 - mean_absolute_error: 0.0236",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0240 - mean_absolute_error: 0.0240 - val_loss: 0.1220 - val_mean_absolute_error: 0.1220\n",
      "\nEpoch 00357: val_loss did not improve from 0.09364\nEpoch 358/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0211 - mean_absolute_error: 0.0211",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0228 - mean_absolute_error: 0.0228",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0221 - mean_absolute_error: 0.0221 - val_loss: 0.1035 - val_mean_absolute_error: 0.1035\n",
      "\nEpoch 00358: val_loss did not improve from 0.09364\nEpoch 359/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0174 - mean_absolute_error: 0.0174",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0265 - mean_absolute_error: 0.0265",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0249 - mean_absolute_error: 0.0249 - val_loss: 0.1081 - val_mean_absolute_error: 0.1081\n",
      "\nEpoch 00359: val_loss did not improve from 0.09364\nEpoch 360/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0316 - mean_absolute_error: 0.0316",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0216 - mean_absolute_error: 0.0216",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0210 - mean_absolute_error: 0.0210 - val_loss: 0.1075 - val_mean_absolute_error: 0.1075\n",
      "\nEpoch 00360: val_loss did not improve from 0.09364\nEpoch 361/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0340 - mean_absolute_error: 0.0340",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0246 - mean_absolute_error: 0.0246",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 53us/step - loss: 0.0235 - mean_absolute_error: 0.0235 - val_loss: 0.1037 - val_mean_absolute_error: 0.1037\n",
      "\nEpoch 00361: val_loss did not improve from 0.09364\nEpoch 362/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0284 - mean_absolute_error: 0.0284",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0245 - mean_absolute_error: 0.0245",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0230 - mean_absolute_error: 0.0230 - val_loss: 0.1034 - val_mean_absolute_error: 0.1034\n",
      "\nEpoch 00362: val_loss did not improve from 0.09364\nEpoch 363/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0268 - mean_absolute_error: 0.0268",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0198 - mean_absolute_error: 0.0198",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 53us/step - loss: 0.0198 - mean_absolute_error: 0.0198 - val_loss: 0.0992 - val_mean_absolute_error: 0.0992\n",
      "\nEpoch 00363: val_loss did not improve from 0.09364\nEpoch 364/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0241 - mean_absolute_error: 0.0241",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0206 - mean_absolute_error: 0.0206",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0210 - mean_absolute_error: 0.0210 - val_loss: 0.1030 - val_mean_absolute_error: 0.1030\n",
      "\nEpoch 00364: val_loss did not improve from 0.09364\nEpoch 365/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0180 - mean_absolute_error: 0.0180",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0257 - mean_absolute_error: 0.0257",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0246 - mean_absolute_error: 0.0246 - val_loss: 0.1069 - val_mean_absolute_error: 0.1069\n",
      "\nEpoch 00365: val_loss did not improve from 0.09364\nEpoch 366/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0276 - mean_absolute_error: 0.0276",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0215 - mean_absolute_error: 0.0215",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0210 - mean_absolute_error: 0.0210 - val_loss: 0.1036 - val_mean_absolute_error: 0.1036\n",
      "\nEpoch 00366: val_loss did not improve from 0.09364\nEpoch 367/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0284 - mean_absolute_error: 0.0284",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0251 - mean_absolute_error: 0.0251",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0242 - mean_absolute_error: 0.0242 - val_loss: 0.1454 - val_mean_absolute_error: 0.1454\n",
      "\nEpoch 00367: val_loss did not improve from 0.09364\nEpoch 368/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0254 - mean_absolute_error: 0.0254",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 896/1417 [=================>............] - ETA: 0s - loss: 0.0262 - mean_absolute_error: 0.0262",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0244 - mean_absolute_error: 0.0244 - val_loss: 0.1113 - val_mean_absolute_error: 0.1113\n",
      "\nEpoch 00368: val_loss did not improve from 0.09364\nEpoch 369/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0258 - mean_absolute_error: 0.0258",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0232 - mean_absolute_error: 0.0232",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0237 - mean_absolute_error: 0.0237 - val_loss: 0.1057 - val_mean_absolute_error: 0.1057\n",
      "\nEpoch 00369: val_loss did not improve from 0.09364\nEpoch 370/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0190 - mean_absolute_error: 0.0190",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0199 - mean_absolute_error: 0.0199",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0198 - mean_absolute_error: 0.0198 - val_loss: 0.1058 - val_mean_absolute_error: 0.1058\n",
      "\nEpoch 00370: val_loss did not improve from 0.09364\nEpoch 371/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0201 - mean_absolute_error: 0.0201",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0259 - mean_absolute_error: 0.0259",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0255 - mean_absolute_error: 0.0255 - val_loss: 0.1057 - val_mean_absolute_error: 0.1057\n",
      "\nEpoch 00371: val_loss did not improve from 0.09364\nEpoch 372/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0179 - mean_absolute_error: 0.0179",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0206 - mean_absolute_error: 0.0206",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0206 - mean_absolute_error: 0.0206 - val_loss: 0.1059 - val_mean_absolute_error: 0.1059\n",
      "\nEpoch 00372: val_loss did not improve from 0.09364\nEpoch 373/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0298 - mean_absolute_error: 0.0298",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0210 - mean_absolute_error: 0.0210",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0209 - mean_absolute_error: 0.0209 - val_loss: 0.1035 - val_mean_absolute_error: 0.1035\n",
      "\nEpoch 00373: val_loss did not improve from 0.09364\nEpoch 374/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0240 - mean_absolute_error: 0.0240",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0191 - mean_absolute_error: 0.0191",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0194 - mean_absolute_error: 0.0194 - val_loss: 0.1007 - val_mean_absolute_error: 0.1007\n",
      "\nEpoch 00374: val_loss did not improve from 0.09364\nEpoch 375/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0230 - mean_absolute_error: 0.0230",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0205 - mean_absolute_error: 0.0205",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0207 - mean_absolute_error: 0.0207 - val_loss: 0.1077 - val_mean_absolute_error: 0.1077\n",
      "\nEpoch 00375: val_loss did not improve from 0.09364\nEpoch 376/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0205 - mean_absolute_error: 0.0205",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0248 - mean_absolute_error: 0.0248",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0245 - mean_absolute_error: 0.0245 - val_loss: 0.1060 - val_mean_absolute_error: 0.1060\n",
      "\nEpoch 00376: val_loss did not improve from 0.09364\nEpoch 377/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0319 - mean_absolute_error: 0.0319",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0206 - mean_absolute_error: 0.0206",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0207 - mean_absolute_error: 0.0207 - val_loss: 0.1137 - val_mean_absolute_error: 0.1137\n",
      "\nEpoch 00377: val_loss did not improve from 0.09364\nEpoch 378/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0188 - mean_absolute_error: 0.0188",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0238 - mean_absolute_error: 0.0238",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0236 - mean_absolute_error: 0.0236 - val_loss: 0.1105 - val_mean_absolute_error: 0.1105\n",
      "\nEpoch 00378: val_loss did not improve from 0.09364\nEpoch 379/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0171 - mean_absolute_error: 0.0171",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0268 - mean_absolute_error: 0.0268",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0258 - mean_absolute_error: 0.0258 - val_loss: 0.1152 - val_mean_absolute_error: 0.1152\n",
      "\nEpoch 00379: val_loss did not improve from 0.09364\nEpoch 380/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0273 - mean_absolute_error: 0.0273",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0231 - mean_absolute_error: 0.0231",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0224 - mean_absolute_error: 0.0224 - val_loss: 0.1024 - val_mean_absolute_error: 0.1024\n",
      "\nEpoch 00380: val_loss did not improve from 0.09364\nEpoch 381/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0220 - mean_absolute_error: 0.0220",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0215 - mean_absolute_error: 0.0215",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0216 - mean_absolute_error: 0.0216 - val_loss: 0.1022 - val_mean_absolute_error: 0.1022\n",
      "\nEpoch 00381: val_loss did not improve from 0.09364\nEpoch 382/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0116 - mean_absolute_error: 0.0116",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0210 - mean_absolute_error: 0.0210",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0240 - mean_absolute_error: 0.0240 - val_loss: 0.1148 - val_mean_absolute_error: 0.1148\n",
      "\nEpoch 00382: val_loss did not improve from 0.09364\nEpoch 383/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0214 - mean_absolute_error: 0.0214",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0248 - mean_absolute_error: 0.0248",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0245 - mean_absolute_error: 0.0245 - val_loss: 0.1115 - val_mean_absolute_error: 0.1115\n",
      "\nEpoch 00383: val_loss did not improve from 0.09364\nEpoch 384/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0219 - mean_absolute_error: 0.0219",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0228 - mean_absolute_error: 0.0228",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0237 - mean_absolute_error: 0.0237 - val_loss: 0.1018 - val_mean_absolute_error: 0.1018\n",
      "\nEpoch 00384: val_loss did not improve from 0.09364\nEpoch 385/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0201 - mean_absolute_error: 0.0201",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0220 - mean_absolute_error: 0.0220",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0214 - mean_absolute_error: 0.0214 - val_loss: 0.1122 - val_mean_absolute_error: 0.1122\n",
      "\nEpoch 00385: val_loss did not improve from 0.09364\nEpoch 386/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0220 - mean_absolute_error: 0.0220",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0199 - mean_absolute_error: 0.0199",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0203 - mean_absolute_error: 0.0203 - val_loss: 0.1019 - val_mean_absolute_error: 0.1019\n",
      "\nEpoch 00386: val_loss did not improve from 0.09364\nEpoch 387/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0251 - mean_absolute_error: 0.0251",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0218 - mean_absolute_error: 0.0218",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0217 - mean_absolute_error: 0.0217 - val_loss: 0.1062 - val_mean_absolute_error: 0.1062\n",
      "\nEpoch 00387: val_loss did not improve from 0.09364\nEpoch 388/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0250 - mean_absolute_error: 0.0250",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0209 - mean_absolute_error: 0.0209",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0214 - mean_absolute_error: 0.0214 - val_loss: 0.1032 - val_mean_absolute_error: 0.1032\n",
      "\nEpoch 00388: val_loss did not improve from 0.09364\nEpoch 389/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0209 - mean_absolute_error: 0.0209",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0224 - mean_absolute_error: 0.0224",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0229 - mean_absolute_error: 0.0229 - val_loss: 0.1224 - val_mean_absolute_error: 0.1224\n",
      "\nEpoch 00389: val_loss did not improve from 0.09364\nEpoch 390/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0242 - mean_absolute_error: 0.0242",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0239 - mean_absolute_error: 0.0239",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0237 - mean_absolute_error: 0.0237 - val_loss: 0.1178 - val_mean_absolute_error: 0.1178\n",
      "\nEpoch 00390: val_loss did not improve from 0.09364\nEpoch 391/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0248 - mean_absolute_error: 0.0248",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0219 - mean_absolute_error: 0.0219",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0223 - mean_absolute_error: 0.0223 - val_loss: 0.1055 - val_mean_absolute_error: 0.1055\n",
      "\nEpoch 00391: val_loss did not improve from 0.09364\nEpoch 392/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0246 - mean_absolute_error: 0.0246",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0220 - mean_absolute_error: 0.0220",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0219 - mean_absolute_error: 0.0219 - val_loss: 0.1003 - val_mean_absolute_error: 0.1003\n",
      "\nEpoch 00392: val_loss did not improve from 0.09364\nEpoch 393/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0140 - mean_absolute_error: 0.0140",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0207 - mean_absolute_error: 0.0207",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0226 - mean_absolute_error: 0.0226 - val_loss: 0.1154 - val_mean_absolute_error: 0.1154\n",
      "\nEpoch 00393: val_loss did not improve from 0.09364\nEpoch 394/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0337 - mean_absolute_error: 0.0337",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0318 - mean_absolute_error: 0.0318",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0288 - mean_absolute_error: 0.0288 - val_loss: 0.1030 - val_mean_absolute_error: 0.1030\n",
      "\nEpoch 00394: val_loss did not improve from 0.09364\nEpoch 395/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0157 - mean_absolute_error: 0.0157",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0226 - mean_absolute_error: 0.0226",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0254 - mean_absolute_error: 0.0254 - val_loss: 0.1136 - val_mean_absolute_error: 0.1136\n",
      "\nEpoch 00395: val_loss did not improve from 0.09364\nEpoch 396/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0361 - mean_absolute_error: 0.0361",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0231 - mean_absolute_error: 0.0231",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 53us/step - loss: 0.0226 - mean_absolute_error: 0.0226 - val_loss: 0.1119 - val_mean_absolute_error: 0.1119\n",
      "\nEpoch 00396: val_loss did not improve from 0.09364\nEpoch 397/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0170 - mean_absolute_error: 0.0170",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0215 - mean_absolute_error: 0.0215",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 53us/step - loss: 0.0209 - mean_absolute_error: 0.0209 - val_loss: 0.1053 - val_mean_absolute_error: 0.1053\n",
      "\nEpoch 00397: val_loss did not improve from 0.09364\nEpoch 398/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0210 - mean_absolute_error: 0.0210",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0256 - mean_absolute_error: 0.0256",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0266 - mean_absolute_error: 0.0266 - val_loss: 0.1057 - val_mean_absolute_error: 0.1057\n",
      "\nEpoch 00398: val_loss did not improve from 0.09364\nEpoch 399/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0298 - mean_absolute_error: 0.0298",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0242 - mean_absolute_error: 0.0242",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0235 - mean_absolute_error: 0.0235 - val_loss: 0.1479 - val_mean_absolute_error: 0.1479\n",
      "\nEpoch 00399: val_loss did not improve from 0.09364\nEpoch 400/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0299 - mean_absolute_error: 0.0299",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0247 - mean_absolute_error: 0.0247",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 55us/step - loss: 0.0240 - mean_absolute_error: 0.0240 - val_loss: 0.1205 - val_mean_absolute_error: 0.1205\n",
      "\nEpoch 00400: val_loss did not improve from 0.09364\nEpoch 401/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0173 - mean_absolute_error: 0.0173",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0218 - mean_absolute_error: 0.0218",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0218 - mean_absolute_error: 0.0218 - val_loss: 0.1103 - val_mean_absolute_error: 0.1103\n",
      "\nEpoch 00401: val_loss did not improve from 0.09364\nEpoch 402/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0188 - mean_absolute_error: 0.0188",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0200 - mean_absolute_error: 0.0200",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0194 - mean_absolute_error: 0.0194 - val_loss: 0.1094 - val_mean_absolute_error: 0.1094\n",
      "\nEpoch 00402: val_loss did not improve from 0.09364\nEpoch 403/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0236 - mean_absolute_error: 0.0236",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1216/1417 [========================>.....] - ETA: 0s - loss: 0.0195 - mean_absolute_error: 0.0195",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 49us/step - loss: 0.0197 - mean_absolute_error: 0.0197 - val_loss: 0.1310 - val_mean_absolute_error: 0.1310\n",
      "\nEpoch 00403: val_loss did not improve from 0.09364\nEpoch 404/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0228 - mean_absolute_error: 0.0228",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0292 - mean_absolute_error: 0.0292",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0276 - mean_absolute_error: 0.0276 - val_loss: 0.1165 - val_mean_absolute_error: 0.1165\n",
      "\nEpoch 00404: val_loss did not improve from 0.09364\nEpoch 405/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0304 - mean_absolute_error: 0.0304",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0263 - mean_absolute_error: 0.0263",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0246 - mean_absolute_error: 0.0246 - val_loss: 0.1350 - val_mean_absolute_error: 0.1350\n",
      "\nEpoch 00405: val_loss did not improve from 0.09364\nEpoch 406/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0331 - mean_absolute_error: 0.0331",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0200 - mean_absolute_error: 0.0200",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0197 - mean_absolute_error: 0.0197 - val_loss: 0.1047 - val_mean_absolute_error: 0.1047\n",
      "\nEpoch 00406: val_loss did not improve from 0.09364\nEpoch 407/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0274 - mean_absolute_error: 0.0274",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0214 - mean_absolute_error: 0.0214",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 53us/step - loss: 0.0214 - mean_absolute_error: 0.0214 - val_loss: 0.1109 - val_mean_absolute_error: 0.1109\n",
      "\nEpoch 00407: val_loss did not improve from 0.09364\n",
      "Epoch 408/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0205 - mean_absolute_error: 0.0205",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0232 - mean_absolute_error: 0.0232",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0234 - mean_absolute_error: 0.0234 - val_loss: 0.1054 - val_mean_absolute_error: 0.1054\n",
      "\nEpoch 00408: val_loss did not improve from 0.09364\nEpoch 409/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0232 - mean_absolute_error: 0.0232",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0216 - mean_absolute_error: 0.0216",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0211 - mean_absolute_error: 0.0211 - val_loss: 0.1056 - val_mean_absolute_error: 0.1056\n",
      "\nEpoch 00409: val_loss did not improve from 0.09364\nEpoch 410/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0186 - mean_absolute_error: 0.0186",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0215 - mean_absolute_error: 0.0215",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0205 - mean_absolute_error: 0.0205 - val_loss: 0.1043 - val_mean_absolute_error: 0.1043\n",
      "\nEpoch 00410: val_loss did not improve from 0.09364\nEpoch 411/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0141 - mean_absolute_error: 0.0141",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0189 - mean_absolute_error: 0.0189",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0184 - mean_absolute_error: 0.0184 - val_loss: 0.1029 - val_mean_absolute_error: 0.1029\n",
      "\nEpoch 00411: val_loss did not improve from 0.09364\nEpoch 412/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0185 - mean_absolute_error: 0.0185",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0216 - mean_absolute_error: 0.0216",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0218 - mean_absolute_error: 0.0218 - val_loss: 0.1091 - val_mean_absolute_error: 0.1091\n",
      "\nEpoch 00412: val_loss did not improve from 0.09364\nEpoch 413/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0237 - mean_absolute_error: 0.0237",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0231 - mean_absolute_error: 0.0231",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 67us/step - loss: 0.0243 - mean_absolute_error: 0.0243 - val_loss: 0.1165 - val_mean_absolute_error: 0.1165\n",
      "\nEpoch 00413: val_loss did not improve from 0.09364\nEpoch 414/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0288 - mean_absolute_error: 0.0288",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0253 - mean_absolute_error: 0.0253",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0254 - mean_absolute_error: 0.0254 - val_loss: 0.1073 - val_mean_absolute_error: 0.1073\n",
      "\nEpoch 00414: val_loss did not improve from 0.09364\nEpoch 415/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0295 - mean_absolute_error: 0.0295",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0212 - mean_absolute_error: 0.0212",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0222 - mean_absolute_error: 0.0222 - val_loss: 0.1039 - val_mean_absolute_error: 0.1039\n",
      "\nEpoch 00415: val_loss did not improve from 0.09364\nEpoch 416/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0192 - mean_absolute_error: 0.0192",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0205 - mean_absolute_error: 0.0205",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0214 - mean_absolute_error: 0.0214 - val_loss: 0.1265 - val_mean_absolute_error: 0.1265\n",
      "\nEpoch 00416: val_loss did not improve from 0.09364\nEpoch 417/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0262 - mean_absolute_error: 0.0262",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0251 - mean_absolute_error: 0.0251",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0243 - mean_absolute_error: 0.0243 - val_loss: 0.1023 - val_mean_absolute_error: 0.1023\n",
      "\nEpoch 00417: val_loss did not improve from 0.09364\nEpoch 418/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0169 - mean_absolute_error: 0.0169",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0184 - mean_absolute_error: 0.0184",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 57us/step - loss: 0.0185 - mean_absolute_error: 0.0185 - val_loss: 0.1030 - val_mean_absolute_error: 0.1030\n",
      "\nEpoch 00418: val_loss did not improve from 0.09364\nEpoch 419/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0109 - mean_absolute_error: 0.0109",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0214 - mean_absolute_error: 0.0214",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0204 - mean_absolute_error: 0.0204 - val_loss: 0.1078 - val_mean_absolute_error: 0.1078\n",
      "\nEpoch 00419: val_loss did not improve from 0.09364\nEpoch 420/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0234 - mean_absolute_error: 0.0234",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0207 - mean_absolute_error: 0.0207",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0225 - mean_absolute_error: 0.0225 - val_loss: 0.1048 - val_mean_absolute_error: 0.1048\n",
      "\nEpoch 00420: val_loss did not improve from 0.09364\nEpoch 421/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0246 - mean_absolute_error: 0.0246",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0253 - mean_absolute_error: 0.0253",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0243 - mean_absolute_error: 0.0243 - val_loss: 0.1149 - val_mean_absolute_error: 0.1149\n",
      "\nEpoch 00421: val_loss did not improve from 0.09364\nEpoch 422/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0173 - mean_absolute_error: 0.0173",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1216/1417 [========================>.....] - ETA: 0s - loss: 0.0191 - mean_absolute_error: 0.0191",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0190 - mean_absolute_error: 0.0190 - val_loss: 0.1087 - val_mean_absolute_error: 0.1087\n",
      "\nEpoch 00422: val_loss did not improve from 0.09364\nEpoch 423/500",
      "\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0226 - mean_absolute_error: 0.0226",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0196 - mean_absolute_error: 0.0196",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0208 - mean_absolute_error: 0.0208 - val_loss: 0.1045 - val_mean_absolute_error: 0.1045\n",
      "\nEpoch 00423: val_loss did not improve from 0.09364\nEpoch 424/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0348 - mean_absolute_error: 0.0348",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0272 - mean_absolute_error: 0.0272",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 53us/step - loss: 0.0275 - mean_absolute_error: 0.0275 - val_loss: 0.1018 - val_mean_absolute_error: 0.1018\n",
      "\nEpoch 00424: val_loss did not improve from 0.09364\nEpoch 425/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0154 - mean_absolute_error: 0.0154",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0221 - mean_absolute_error: 0.0221",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0217 - mean_absolute_error: 0.0217 - val_loss: 0.1075 - val_mean_absolute_error: 0.1075\n",
      "\nEpoch 00425: val_loss did not improve from 0.09364\nEpoch 426/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0190 - mean_absolute_error: 0.0190",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1216/1417 [========================>.....] - ETA: 0s - loss: 0.0236 - mean_absolute_error: 0.0236",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 50us/step - loss: 0.0231 - mean_absolute_error: 0.0231 - val_loss: 0.1169 - val_mean_absolute_error: 0.1169\n",
      "\nEpoch 00426: val_loss did not improve from 0.09364\nEpoch 427/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0167 - mean_absolute_error: 0.0167",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0221 - mean_absolute_error: 0.0221",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0215 - mean_absolute_error: 0.0215 - val_loss: 0.1029 - val_mean_absolute_error: 0.1029\n",
      "\nEpoch 00427: val_loss did not improve from 0.09364\nEpoch 428/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0208 - mean_absolute_error: 0.0208",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0237 - mean_absolute_error: 0.0237",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0235 - mean_absolute_error: 0.0235 - val_loss: 0.1028 - val_mean_absolute_error: 0.1028\n",
      "\nEpoch 00428: val_loss did not improve from 0.09364\nEpoch 429/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0214 - mean_absolute_error: 0.0214",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1216/1417 [========================>.....] - ETA: 0s - loss: 0.0219 - mean_absolute_error: 0.0219",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 50us/step - loss: 0.0224 - mean_absolute_error: 0.0224 - val_loss: 0.1040 - val_mean_absolute_error: 0.1040\n",
      "\nEpoch 00429: val_loss did not improve from 0.09364\nEpoch 430/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0293 - mean_absolute_error: 0.0293",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0270 - mean_absolute_error: 0.0270",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0253 - mean_absolute_error: 0.0253 - val_loss: 0.1017 - val_mean_absolute_error: 0.1017\n",
      "\nEpoch 00430: val_loss did not improve from 0.09364\nEpoch 431/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0180 - mean_absolute_error: 0.0180",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0211 - mean_absolute_error: 0.0211",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0220 - mean_absolute_error: 0.0220 - val_loss: 0.1169 - val_mean_absolute_error: 0.1169\n",
      "\nEpoch 00431: val_loss did not improve from 0.09364\nEpoch 432/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0351 - mean_absolute_error: 0.0351",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0246 - mean_absolute_error: 0.0246",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 50us/step - loss: 0.0251 - mean_absolute_error: 0.0251 - val_loss: 0.1160 - val_mean_absolute_error: 0.1160\n",
      "\nEpoch 00432: val_loss did not improve from 0.09364\nEpoch 433/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0237 - mean_absolute_error: 0.0237",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0190 - mean_absolute_error: 0.0190",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0197 - mean_absolute_error: 0.0197 - val_loss: 0.1024 - val_mean_absolute_error: 0.1024\n",
      "\nEpoch 00433: val_loss did not improve from 0.09364\nEpoch 434/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0276 - mean_absolute_error: 0.0276",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0199 - mean_absolute_error: 0.0199",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 50us/step - loss: 0.0202 - mean_absolute_error: 0.0202 - val_loss: 0.1021 - val_mean_absolute_error: 0.1021\n",
      "\nEpoch 00434: val_loss did not improve from 0.09364\nEpoch 435/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0143 - mean_absolute_error: 0.0143",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1216/1417 [========================>.....] - ETA: 0s - loss: 0.0190 - mean_absolute_error: 0.0190",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 49us/step - loss: 0.0189 - mean_absolute_error: 0.0189 - val_loss: 0.1199 - val_mean_absolute_error: 0.1199\n",
      "\nEpoch 00435: val_loss did not improve from 0.09364\nEpoch 436/500\n\r",
      "  32/1417 [..............................] - ETA: 0s - loss: 0.0267 - mean_absolute_error: 0.0267",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0212 - mean_absolute_error: 0.0212",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0205 - mean_absolute_error: 0.0205 - val_loss: 0.1048 - val_mean_absolute_error: 0.1048\n",
      "\nEpoch 00436: val_loss did not improve from 0.09364\nEpoch 437/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0148 - mean_absolute_error: 0.0148",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0227 - mean_absolute_error: 0.0227",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0218 - mean_absolute_error: 0.0218 - val_loss: 0.1087 - val_mean_absolute_error: 0.1087\n",
      "\nEpoch 00437: val_loss did not improve from 0.09364\nEpoch 438/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0155 - mean_absolute_error: 0.0155",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0190 - mean_absolute_error: 0.0190",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0199 - mean_absolute_error: 0.0199 - val_loss: 0.1068 - val_mean_absolute_error: 0.1068\n",
      "\nEpoch 00438: val_loss did not improve from 0.09364\nEpoch 439/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0283 - mean_absolute_error: 0.0283",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0228 - mean_absolute_error: 0.0228",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 53us/step - loss: 0.0223 - mean_absolute_error: 0.0223 - val_loss: 0.1041 - val_mean_absolute_error: 0.1041\n",
      "\nEpoch 00439: val_loss did not improve from 0.09364\nEpoch 440/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0285 - mean_absolute_error: 0.0285",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0215 - mean_absolute_error: 0.0215",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 53us/step - loss: 0.0207 - mean_absolute_error: 0.0207 - val_loss: 0.1027 - val_mean_absolute_error: 0.1027\n",
      "\nEpoch 00440: val_loss did not improve from 0.09364\nEpoch 441/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0245 - mean_absolute_error: 0.0245",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0221 - mean_absolute_error: 0.0221",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 53us/step - loss: 0.0221 - mean_absolute_error: 0.0221 - val_loss: 0.1120 - val_mean_absolute_error: 0.1120\n",
      "\nEpoch 00441: val_loss did not improve from 0.09364\nEpoch 442/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0144 - mean_absolute_error: 0.0144",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0199 - mean_absolute_error: 0.0199",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0193 - mean_absolute_error: 0.0193 - val_loss: 0.1030 - val_mean_absolute_error: 0.1030\n",
      "\nEpoch 00442: val_loss did not improve from 0.09364\nEpoch 443/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0222 - mean_absolute_error: 0.0222",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1216/1417 [========================>.....] - ETA: 0s - loss: 0.0211 - mean_absolute_error: 0.0211",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 50us/step - loss: 0.0211 - mean_absolute_error: 0.0211 - val_loss: 0.1284 - val_mean_absolute_error: 0.1284\n",
      "\nEpoch 00443: val_loss did not improve from 0.09364\nEpoch 444/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0170 - mean_absolute_error: 0.0170",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 960/1417 [===================>..........] - ETA: 0s - loss: 0.0193 - mean_absolute_error: 0.0193",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 63us/step - loss: 0.0202 - mean_absolute_error: 0.0202 - val_loss: 0.1027 - val_mean_absolute_error: 0.1027\n",
      "\nEpoch 00444: val_loss did not improve from 0.09364\nEpoch 445/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0139 - mean_absolute_error: 0.0139",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0198 - mean_absolute_error: 0.0198",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0204 - mean_absolute_error: 0.0204 - val_loss: 0.1076 - val_mean_absolute_error: 0.1076\n",
      "\nEpoch 00445: val_loss did not improve from 0.09364\nEpoch 446/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0205 - mean_absolute_error: 0.0205",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0202 - mean_absolute_error: 0.0202",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0206 - mean_absolute_error: 0.0206 - val_loss: 0.1054 - val_mean_absolute_error: 0.1054\n",
      "\nEpoch 00446: val_loss did not improve from 0.09364\nEpoch 447/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0137 - mean_absolute_error: 0.0137",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0216 - mean_absolute_error: 0.0216",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0206 - mean_absolute_error: 0.0206 - val_loss: 0.1053 - val_mean_absolute_error: 0.1053\n",
      "\nEpoch 00447: val_loss did not improve from 0.09364\nEpoch 448/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0153 - mean_absolute_error: 0.0153",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0214 - mean_absolute_error: 0.0214",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0223 - mean_absolute_error: 0.0223 - val_loss: 0.1274 - val_mean_absolute_error: 0.1274\n",
      "\nEpoch 00448: val_loss did not improve from 0.09364\nEpoch 449/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0284 - mean_absolute_error: 0.0284",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0225 - mean_absolute_error: 0.0225",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0226 - mean_absolute_error: 0.0226 - val_loss: 0.1111 - val_mean_absolute_error: 0.1111\n",
      "\nEpoch 00449: val_loss did not improve from 0.09364\nEpoch 450/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0319 - mean_absolute_error: 0.0319",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0237 - mean_absolute_error: 0.0237",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0232 - mean_absolute_error: 0.0232 - val_loss: 0.1248 - val_mean_absolute_error: 0.1248\n",
      "\nEpoch 00450: val_loss did not improve from 0.09364\nEpoch 451/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0235 - mean_absolute_error: 0.0235",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0221 - mean_absolute_error: 0.0221",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0218 - mean_absolute_error: 0.0218 - val_loss: 0.1035 - val_mean_absolute_error: 0.1035\n",
      "\nEpoch 00451: val_loss did not improve from 0.09364\nEpoch 452/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0200 - mean_absolute_error: 0.0200",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0191 - mean_absolute_error: 0.0191",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0194 - mean_absolute_error: 0.0194 - val_loss: 0.1305 - val_mean_absolute_error: 0.1305\n",
      "\nEpoch 00452: val_loss did not improve from 0.09364\nEpoch 453/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0133 - mean_absolute_error: 0.0133",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1216/1417 [========================>.....] - ETA: 0s - loss: 0.0206 - mean_absolute_error: 0.0206",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0211 - mean_absolute_error: 0.0211 - val_loss: 0.1084 - val_mean_absolute_error: 0.1084\n",
      "\nEpoch 00453: val_loss did not improve from 0.09364\nEpoch 454/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0349 - mean_absolute_error: 0.0349",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0235 - mean_absolute_error: 0.0235",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0227 - mean_absolute_error: 0.0227 - val_loss: 0.1263 - val_mean_absolute_error: 0.1263\n",
      "\nEpoch 00454: val_loss did not improve from 0.09364\nEpoch 455/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0394 - mean_absolute_error: 0.0394",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1216/1417 [========================>.....] - ETA: 0s - loss: 0.0224 - mean_absolute_error: 0.0224",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 50us/step - loss: 0.0223 - mean_absolute_error: 0.0223 - val_loss: 0.1047 - val_mean_absolute_error: 0.1047\n",
      "\nEpoch 00455: val_loss did not improve from 0.09364\nEpoch 456/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0303 - mean_absolute_error: 0.0303",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0232 - mean_absolute_error: 0.0232",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0244 - mean_absolute_error: 0.0244 - val_loss: 0.1041 - val_mean_absolute_error: 0.1041\n",
      "\nEpoch 00456: val_loss did not improve from 0.09364\nEpoch 457/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0174 - mean_absolute_error: 0.0174",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1216/1417 [========================>.....] - ETA: 0s - loss: 0.0231 - mean_absolute_error: 0.0231",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 49us/step - loss: 0.0228 - mean_absolute_error: 0.0228 - val_loss: 0.1060 - val_mean_absolute_error: 0.1060\n",
      "\nEpoch 00457: val_loss did not improve from 0.09364\nEpoch 458/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0136 - mean_absolute_error: 0.0136",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0188 - mean_absolute_error: 0.0188",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0193 - mean_absolute_error: 0.0193 - val_loss: 0.1085 - val_mean_absolute_error: 0.1085\n",
      "\nEpoch 00458: val_loss did not improve from 0.09364\nEpoch 459/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0201 - mean_absolute_error: 0.0201",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0191 - mean_absolute_error: 0.0191",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0190 - mean_absolute_error: 0.0190 - val_loss: 0.1089 - val_mean_absolute_error: 0.1089\n",
      "\nEpoch 00459: val_loss did not improve from 0.09364\nEpoch 460/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0186 - mean_absolute_error: 0.0186",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0200 - mean_absolute_error: 0.0200",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0195 - mean_absolute_error: 0.0195 - val_loss: 0.1036 - val_mean_absolute_error: 0.1036\n",
      "\nEpoch 00460: val_loss did not improve from 0.09364\nEpoch 461/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0266 - mean_absolute_error: 0.0266",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0200 - mean_absolute_error: 0.0200",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0196 - mean_absolute_error: 0.0196 - val_loss: 0.1020 - val_mean_absolute_error: 0.1020\n",
      "\nEpoch 00461: val_loss did not improve from 0.09364\nEpoch 462/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0170 - mean_absolute_error: 0.0170",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0218 - mean_absolute_error: 0.0218",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0208 - mean_absolute_error: 0.0208 - val_loss: 0.1064 - val_mean_absolute_error: 0.1064\n",
      "\nEpoch 00462: val_loss did not improve from 0.09364\nEpoch 463/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0184 - mean_absolute_error: 0.0184",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0181 - mean_absolute_error: 0.0181",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 49us/step - loss: 0.0186 - mean_absolute_error: 0.0186 - val_loss: 0.1050 - val_mean_absolute_error: 0.1050\n",
      "\nEpoch 00463: val_loss did not improve from 0.09364\nEpoch 464/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0295 - mean_absolute_error: 0.0295",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0317 - mean_absolute_error: 0.0317",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0301 - mean_absolute_error: 0.0301 - val_loss: 0.1331 - val_mean_absolute_error: 0.1331\n",
      "\nEpoch 00464: val_loss did not improve from 0.09364\nEpoch 465/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0322 - mean_absolute_error: 0.0322",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0209 - mean_absolute_error: 0.0209",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0206 - mean_absolute_error: 0.0206 - val_loss: 0.1141 - val_mean_absolute_error: 0.1141\n",
      "\nEpoch 00465: val_loss did not improve from 0.09364\nEpoch 466/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0228 - mean_absolute_error: 0.0228",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0214 - mean_absolute_error: 0.0214",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0221 - mean_absolute_error: 0.0221 - val_loss: 0.1110 - val_mean_absolute_error: 0.1110\n",
      "\nEpoch 00466: val_loss did not improve from 0.09364\nEpoch 467/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0143 - mean_absolute_error: 0.0143",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0235 - mean_absolute_error: 0.0235",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0220 - mean_absolute_error: 0.0220 - val_loss: 0.1092 - val_mean_absolute_error: 0.1092\n",
      "\nEpoch 00467: val_loss did not improve from 0.09364\nEpoch 468/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0191 - mean_absolute_error: 0.0191",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r 992/1417 [====================>.........] - ETA: 0s - loss: 0.0193 - mean_absolute_error: 0.0193",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 60us/step - loss: 0.0187 - mean_absolute_error: 0.0187 - val_loss: 0.1191 - val_mean_absolute_error: 0.1191\n",
      "\nEpoch 00468: val_loss did not improve from 0.09364\nEpoch 469/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0158 - mean_absolute_error: 0.0158",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0197 - mean_absolute_error: 0.0197",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0204 - mean_absolute_error: 0.0204 - val_loss: 0.1224 - val_mean_absolute_error: 0.1224\n",
      "\nEpoch 00469: val_loss did not improve from 0.09364\nEpoch 470/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0234 - mean_absolute_error: 0.0234",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0184 - mean_absolute_error: 0.0184",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0186 - mean_absolute_error: 0.0186 - val_loss: 0.1191 - val_mean_absolute_error: 0.1191\n",
      "\nEpoch 00470: val_loss did not improve from 0.09364\nEpoch 471/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0221 - mean_absolute_error: 0.0221",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0244 - mean_absolute_error: 0.0244",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0252 - mean_absolute_error: 0.0252 - val_loss: 0.1057 - val_mean_absolute_error: 0.1057\n",
      "\nEpoch 00471: val_loss did not improve from 0.09364\nEpoch 472/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0304 - mean_absolute_error: 0.0304",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0232 - mean_absolute_error: 0.0232",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0225 - mean_absolute_error: 0.0225 - val_loss: 0.1132 - val_mean_absolute_error: 0.1132\n",
      "\nEpoch 00472: val_loss did not improve from 0.09364\nEpoch 473/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0237 - mean_absolute_error: 0.0237",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1216/1417 [========================>.....] - ETA: 0s - loss: 0.0208 - mean_absolute_error: 0.0208",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0210 - mean_absolute_error: 0.0210 - val_loss: 0.1100 - val_mean_absolute_error: 0.1100\n",
      "\nEpoch 00473: val_loss did not improve from 0.09364\nEpoch 474/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0123 - mean_absolute_error: 0.0123",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0269 - mean_absolute_error: 0.0269",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 53us/step - loss: 0.0264 - mean_absolute_error: 0.0264 - val_loss: 0.1028 - val_mean_absolute_error: 0.1028\n",
      "\nEpoch 00474: val_loss did not improve from 0.09364\nEpoch 475/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0203 - mean_absolute_error: 0.0203",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0227 - mean_absolute_error: 0.0227",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0224 - mean_absolute_error: 0.0224 - val_loss: 0.1007 - val_mean_absolute_error: 0.1007\n",
      "\nEpoch 00475: val_loss did not improve from 0.09364\nEpoch 476/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0236 - mean_absolute_error: 0.0236",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0211 - mean_absolute_error: 0.0211",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 74us/step - loss: 0.0208 - mean_absolute_error: 0.0208 - val_loss: 0.1063 - val_mean_absolute_error: 0.1063\n",
      "\nEpoch 00476: val_loss did not improve from 0.09364\nEpoch 477/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0140 - mean_absolute_error: 0.0140",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r 928/1417 [==================>...........] - ETA: 0s - loss: 0.0246 - mean_absolute_error: 0.0246",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 61us/step - loss: 0.0219 - mean_absolute_error: 0.0219 - val_loss: 0.1146 - val_mean_absolute_error: 0.1146\n",
      "\nEpoch 00477: val_loss did not improve from 0.09364\nEpoch 478/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0166 - mean_absolute_error: 0.0166",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0205 - mean_absolute_error: 0.0205",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0215 - mean_absolute_error: 0.0215 - val_loss: 0.1152 - val_mean_absolute_error: 0.1152\n",
      "\nEpoch 00478: val_loss did not improve from 0.09364\nEpoch 479/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0148 - mean_absolute_error: 0.0148",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0218 - mean_absolute_error: 0.0218",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0226 - mean_absolute_error: 0.0226 - val_loss: 0.1262 - val_mean_absolute_error: 0.1262\n",
      "\nEpoch 00479: val_loss did not improve from 0.09364\nEpoch 480/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0304 - mean_absolute_error: 0.0304",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0196 - mean_absolute_error: 0.0196",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0195 - mean_absolute_error: 0.0195 - val_loss: 0.1196 - val_mean_absolute_error: 0.1196\n",
      "\nEpoch 00480: val_loss did not improve from 0.09364\nEpoch 481/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0203 - mean_absolute_error: 0.0203",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1216/1417 [========================>.....] - ETA: 0s - loss: 0.0244 - mean_absolute_error: 0.0244",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 50us/step - loss: 0.0242 - mean_absolute_error: 0.0242 - val_loss: 0.1062 - val_mean_absolute_error: 0.1062\n",
      "\nEpoch 00481: val_loss did not improve from 0.09364\nEpoch 482/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0233 - mean_absolute_error: 0.0233",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0217 - mean_absolute_error: 0.0217",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0216 - mean_absolute_error: 0.0216 - val_loss: 0.1058 - val_mean_absolute_error: 0.1058\n",
      "\nEpoch 00482: val_loss did not improve from 0.09364\nEpoch 483/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0144 - mean_absolute_error: 0.0144",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0218 - mean_absolute_error: 0.0218",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0212 - mean_absolute_error: 0.0212 - val_loss: 0.1388 - val_mean_absolute_error: 0.1388\n",
      "\nEpoch 00483: val_loss did not improve from 0.09364\nEpoch 484/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0244 - mean_absolute_error: 0.0244",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1120/1417 [======================>.......] - ETA: 0s - loss: 0.0239 - mean_absolute_error: 0.0239",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0230 - mean_absolute_error: 0.0230 - val_loss: 0.1117 - val_mean_absolute_error: 0.1117\n",
      "\nEpoch 00484: val_loss did not improve from 0.09364\nEpoch 485/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0333 - mean_absolute_error: 0.0333",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0231 - mean_absolute_error: 0.0231",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 50us/step - loss: 0.0227 - mean_absolute_error: 0.0227 - val_loss: 0.1145 - val_mean_absolute_error: 0.1145\n",
      "\nEpoch 00485: val_loss did not improve from 0.09364\nEpoch 486/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0205 - mean_absolute_error: 0.0205",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0198 - mean_absolute_error: 0.0198",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0194 - mean_absolute_error: 0.0194 - val_loss: 0.1120 - val_mean_absolute_error: 0.1120\n",
      "\nEpoch 00486: val_loss did not improve from 0.09364\nEpoch 487/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0217 - mean_absolute_error: 0.0217",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0188 - mean_absolute_error: 0.0188",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 49us/step - loss: 0.0185 - mean_absolute_error: 0.0185 - val_loss: 0.1065 - val_mean_absolute_error: 0.1065\n",
      "\nEpoch 00487: val_loss did not improve from 0.09364\nEpoch 488/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0254 - mean_absolute_error: 0.0254",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1216/1417 [========================>.....] - ETA: 0s - loss: 0.0191 - mean_absolute_error: 0.0191",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 50us/step - loss: 0.0194 - mean_absolute_error: 0.0194 - val_loss: 0.1037 - val_mean_absolute_error: 0.1037\n",
      "\nEpoch 00488: val_loss did not improve from 0.09364\nEpoch 489/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0190 - mean_absolute_error: 0.0190",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0201 - mean_absolute_error: 0.0201",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0200 - mean_absolute_error: 0.0200 - val_loss: 0.1239 - val_mean_absolute_error: 0.1239\n",
      "\nEpoch 00489: val_loss did not improve from 0.09364\nEpoch 490/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0276 - mean_absolute_error: 0.0276",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1024/1417 [====================>.........] - ETA: 0s - loss: 0.0247 - mean_absolute_error: 0.0247",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 56us/step - loss: 0.0227 - mean_absolute_error: 0.0227 - val_loss: 0.1078 - val_mean_absolute_error: 0.1078\n",
      "\nEpoch 00490: val_loss did not improve from 0.09364\nEpoch 491/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0163 - mean_absolute_error: 0.0163",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0216 - mean_absolute_error: 0.0216",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0213 - mean_absolute_error: 0.0213 - val_loss: 0.1083 - val_mean_absolute_error: 0.1083\n",
      "\nEpoch 00491: val_loss did not improve from 0.09364\nEpoch 492/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0198 - mean_absolute_error: 0.0198",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0201 - mean_absolute_error: 0.0201",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0201 - mean_absolute_error: 0.0201 - val_loss: 0.1052 - val_mean_absolute_error: 0.1052\n",
      "\nEpoch 00492: val_loss did not improve from 0.09364\nEpoch 493/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0151 - mean_absolute_error: 0.0151",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0222 - mean_absolute_error: 0.0222",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0221 - mean_absolute_error: 0.0221 - val_loss: 0.1122 - val_mean_absolute_error: 0.1122\n",
      "\nEpoch 00493: val_loss did not improve from 0.09364\nEpoch 494/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0277 - mean_absolute_error: 0.0277",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0230 - mean_absolute_error: 0.0230",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 58us/step - loss: 0.0219 - mean_absolute_error: 0.0219 - val_loss: 0.1057 - val_mean_absolute_error: 0.1057\n",
      "\nEpoch 00494: val_loss did not improve from 0.09364\nEpoch 495/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0237 - mean_absolute_error: 0.0237",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1088/1417 [======================>.......] - ETA: 0s - loss: 0.0181 - mean_absolute_error: 0.0181",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 53us/step - loss: 0.0187 - mean_absolute_error: 0.0187 - val_loss: 0.1063 - val_mean_absolute_error: 0.1063\n",
      "\nEpoch 00495: val_loss did not improve from 0.09364\nEpoch 496/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0133 - mean_absolute_error: 0.0133",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0235 - mean_absolute_error: 0.0235",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 54us/step - loss: 0.0226 - mean_absolute_error: 0.0226 - val_loss: 0.1273 - val_mean_absolute_error: 0.1273\n",
      "\nEpoch 00496: val_loss did not improve from 0.09364\nEpoch 497/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0301 - mean_absolute_error: 0.0301",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1216/1417 [========================>.....] - ETA: 0s - loss: 0.0270 - mean_absolute_error: 0.0270",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 51us/step - loss: 0.0267 - mean_absolute_error: 0.0267 - val_loss: 0.1054 - val_mean_absolute_error: 0.1054\n",
      "\nEpoch 00497: val_loss did not improve from 0.09364\nEpoch 498/500\n",
      "\r  32/1417 [..............................] - ETA: 0s - loss: 0.0169 - mean_absolute_error: 0.0169",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1152/1417 [=======================>......] - ETA: 0s - loss: 0.0190 - mean_absolute_error: 0.0190",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0192 - mean_absolute_error: 0.0192 - val_loss: 0.1061 - val_mean_absolute_error: 0.1061\n",
      "\nEpoch 00498: val_loss did not improve from 0.09364\nEpoch 499/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0227 - mean_absolute_error: 0.0227",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1184/1417 [========================>.....] - ETA: 0s - loss: 0.0285 - mean_absolute_error: 0.0285",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1417/1417 [==============================] - 0s 52us/step - loss: 0.0266 - mean_absolute_error: 0.0266 - val_loss: 0.1192 - val_mean_absolute_error: 0.1192\n",
      "\nEpoch 00499: val_loss did not improve from 0.09364\nEpoch 500/500\n\r  32/1417 [..............................] - ETA: 0s - loss: 0.0231 - mean_absolute_error: 0.0231",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1056/1417 [=====================>........] - ETA: 0s - loss: 0.0220 - mean_absolute_error: 0.0220",
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b",
      "\r1417/1417 [==============================] - 0s 59us/step - loss: 0.0211 - mean_absolute_error: 0.0211 - val_loss: 0.1088 - val_mean_absolute_error: 0.1088\n",
      "\nEpoch 00500: val_loss did not improve from 0.09364\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "<keras.callbacks.callbacks.History at 0x22341d5fa48>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 34
    }
   ],
   "source": [
    "NN_model.fit(X_train_g, y_train_g, epochs=500, batch_size=32, validation_split = 0.2, callbacks=callbacks_list)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [],
   "source": [
    "wights_file = 'Weights-004--0.13668.hdf5' # choose the best checkpoint\n",
    "NN_model.load_weights(wights_file) # load it\n",
    "NN_model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mean_absolute_error'])\n",
    "\n",
    "predict = NN_model.predict(X_test_g)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 3 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-36-46a3ec24fcfe>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mpredictVal\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplotPicturesEx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test_t\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredictVal\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdefaultTitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maccuracyAssessment\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test_g\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredictVal\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\02Projects\\papers\\chlmodel\\models\\ML\\utils.py\u001b[0m in \u001b[0;36maccuracyAssessment\u001b[1;34m(tureVals, preVals, is_print)\u001b[0m\n\u001b[0;32m     25\u001b[0m     \u001b[1;31m# 均方误差\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m     \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmean_squared_log_error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 27\u001b[1;33m     \u001b[0mmsle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmean_squared_log_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtureVals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpreVals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m     \u001b[1;31m# 中位数绝对误差\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Users\\ASUS\\anaconda3\\envs\\deeplab\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Users\\ASUS\\anaconda3\\envs\\deeplab\\lib\\site-packages\\sklearn\\metrics\\_regression.py\u001b[0m in \u001b[0;36mmean_squared_log_error\u001b[1;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[0;32m    332\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    333\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 334\u001b[1;33m         raise ValueError(\"Mean Squared Logarithmic Error cannot be used when \"\n\u001b[0m\u001b[0;32m    335\u001b[0m                          \"targets contain negative values.\")\n\u001b[0;32m    336\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Mean Squared Logarithmic Error cannot be used when targets contain negative values."
     ],
     "ename": "ValueError",
     "evalue": "Mean Squared Logarithmic Error cannot be used when targets contain negative values.",
     "output_type": "error"
    }
   ],
   "source": [
    "import  utils\n",
    "y_test_t = y_test_g.flatten()\n",
    "predictVal = predict.flatten()\n",
    "utils.plotPicturesEx(y_test_t,predictVal,defaultTitle='NN')\n",
    "utils.accuracyAssessment(y_test_g,predictVal)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}